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In this paper, we compared the linear and nonlinear motion prediction models of a long combination vehicle (LCV). We designed a nonlinear model predictive control (NMPC) for trajectory-following and off-tracking minimisation of the LCV. The used prediction model allowed coupled longitudinal and lateral dynamics together with the possibility of a combined steering, propulsion and braking control of those vehicles in long prediction horizons and in all ranges of forward velocity. For LCVs where the vehicle model is highly nonlinear, we showed that the control actions calculated by a linear time-varying model predictive control (LTV-MPC) are relatively close to those obtained by the NMPC if the guess linearisation trajectory is sufficiently close to the nonlinear solution, in contrast to linearising for specific operating conditions that limit the generality of the designed function. We discussed how those guess trajectories can be obtained allowing off-line fixed time-varying model linearisation that is beneficial for real-time implementation of MPC in LCVs with long prediction horizons. The long prediction horizons are necessary for motion planning and trajectory-following of LCVs to maintain stability and tracking quality, e.g. by optimally reducing the speed prior to reaching a curve, and by generating control actions within the actuators limits. [ABSTRACT FROM AUTHOR]

The article discusses the implementation of independent component analysis (ICA) in the brain and its potential for modeling brain computations. The author suggests that while modeling the brain on statistical and objective levels is promising, modeling the algorithmic level is more challenging due to the difficulty of measuring single neurons and their learning. The author also discusses the interpretation of nonlinear ICA as defining an exponential model and the use of self-supervised learning methods in machine learning. Overall, the article highlights the complexities and challenges of nonlinear ICA as a statistical model and its potential applications in understanding brain computations. [Extracted from the article]

A stochastic model predictive control (MPC) framework is presented in this paper for nonlinear affine systems with stability and feasibility guarantee. We first introduce the concept of stochastic control Lyapunov–Barrier function (CLBF) and provide a method to construct CLBF by combining an unconstrained control Lyapunov function (CLF) and control barrier functions. The unconstrained CLF is obtained from its corresponding semi‐linear system through dynamic feedback linearization. Based on the constructed CLBF, we utilize sampled‐data MPC framework to deal with states and inputs constraints, and to analyze stability of closed‐loop systems. Moreover, event‐triggering mechanisms are integrated into MPC framework to improve performance during sampling intervals. The proposed CLBF based stochastic MPC is validated via an obstacle avoidance example. [ABSTRACT FROM AUTHOR]

We construct a new class of nonlinear coherent states for the isotonic oscillator by replacing the factorial of the coefficients z n / n ! of the canonical coherent states by the factorial x n γ ! = x 1 γ. x 2 γ ... x n γ with x 0 γ = 0 , where x n γ is a sequence of positive numbers and γ is a positive real parameter. This also leads to the construction of a Bargmann-type integral transform which will allow us to find some integral transforms for orthogonal polynomials. The statistics of our coherent states will also be considered by the calculus of one called Mandel parameter. The squeezing phenomenon was also discussed. [ABSTRACT FROM AUTHOR]

Due to the effects of global climate change and altered human land-use patterns, typical shrub encroachment in grasslands has become one of the most prominent ecological problems in grassland ecosystems. Shrub coverage can quantitatively indicate the degree of shrub encroachment in grasslands; therefore, real-time and accurate monitoring of shrub coverage in large areas has important scientific significance for the protection and restoration of grassland ecosystems. As shrub-encroached grasslands (SEGs) are a type of grassland with continuous and alternating growth of shrubs and grasses, estimating shrub coverage is different from estimating vegetation coverage. It is not only necessary to consider the differences in the characteristics of vegetation and non-vegetation variables but also the differences in characteristics of shrubs and herbs, which can be a challenging estimation. There is a scientific need to estimate shrub coverage in SEGs to improve our understanding of the process of shrub encroachment in grasslands. This article discusses the spectral differences between herbs and shrubs and further points out the possibility of distinguishing between herbs and shrubs. We use Sentinel-2 and Gao Fen-6 (GF-6) Wide Field of View (WFV) as data sources to build a linear spectral mixture model and a random forest (RF) model via space–air–ground collaboration and investigate the effectiveness of different data sources, features and methods in estimating shrub coverage in SEGs, which provide promising ways to monitor the dynamics of SEGs. The results showed that (1) the linear spectral mixture model can hardly distinguish between shrubs and herbs from medium-resolution images in the SEG. (2) The RF model showed high estimation accuracy for shrub coverage in the SEG; the estimation accuracy (R2) of the Sentinel-2 image was 0.81, and the root-mean-square error (RMSE) was 0.03. The R2 of the GF6-WFV image was 0.72, and the RMSE was 0.03. (3) Texture feature introduced in RF models are helpful to estimate shrub coverage in SEGs. (4) Regardless of the linear spectral mixture model or the RF model being employed, the Sentinel-2 image presented a better estimation than the GF6-WFV image; thus, this data has great potential to monitor shrub encroachment in grasslands. This research aims to provide a scientific basis and reference for remote sensing-based monitoring of SEGs. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) reconstruction of lower limbs is of great interest in surgical planning, computer assisted surgery, and for biomechanical applications. The use of 3D imaging modalities such as computed tomography (CT) scan and magnetic resonance imaging (MRI) has limitations such as high radiation and expense. Therefore, three-dimensional reconstruction methods from biplanar X-ray images represent an attractive alternative. In this paper, we present a new unsupervised 3D reconstruction method for the patella, talus, and pelvis using calibrated biplanar (45- and 135-degree oblique) radiographic images and a prior information on the geometric/anatomical structure of these complex bones. A multidimensional scaling (MDS)-based nonlinear dimensionality reduction algorithm is applied to exploit this prior geometric/anatomical information. It represents relevant deformations existing in the training set. Our method is based on a hybrid-likelihood using regions and contours. The edge-based notion represents the relation between the external contours of the bone projections and an edge potential field estimated on the radiographic images. Region-based notion is the non-overlapping ratio between segmented and projected bone regions of interest (RoIs). Our automatic 3D reconstruction model entails stochastically minimizing an energy function allowing an estimation of deformation parameters of the bone shape. This 3D reconstruction method has been successfully tested on 13 biplanar radiographic image pairs, yielding very promising results. [ABSTRACT FROM AUTHOR]

Seepage prediction is a vital part of the dam safety monitoring system. Traditional statistical models ignore the nonlinear characteristics of the measured variables, resulting in poor accuracy and stability. In this study, an optimized neural network model is constructed to predict the seepage extent of hydropower station dams. An improved gradient-based optimizer (IGBO) is proposed to increase the accuracy and reliability of extreme learning machine (ELM) model predictions. The IGBO introduces an initialization method with elite opposition-based learning to improve population diversity. A crossover operator and a nonlinear parameter are used in the IGBO to enhance the ability of local search and the probability of avoiding local optima. The performance of the IGBO-optimized ELM network (IGBO-ELM) was evaluated on 12 datasets. In addition, the comparison experimental results with actual monitoring data of concrete dams show that IGBO-ELM has strong generalization performance and accuracy among the other four optimization models. [ABSTRACT FROM AUTHOR]

• NSDAHU is proposed to solve nonlinear structural dynamics problems with hybrid uncertainties. • NSDAHU easily transmits response distribution of nonlinear structural dynamic systems directly using control equations. • NSDAHU solves pure interval, pure random, and linear structural dynamics problems. • Three numerical examples verify the applicability, accuracy, and efficiency of NSDAHU. The physical systems of nonlinear structural dynamics are usually expressed mathematically as ordinary differential equations. When hybrid uncertainties are in the system, it is necessary to understand the effects of uncertainties in the system response to accurately predict its behavior using mathematical models. To address the hybrid uncertainty of random and interval parameters often encountered in engineering applications, we propose the nonlinear structural dynamics analysis with hybrid uncertainty (NSDAHU) method. The expressions for calculating the statistical characteristics (i.e., mean and variance) of nonlinear structural dynamic responses with hybrid uncertainties are derived using random interval moment and random interval perturbation methods. The applicability, accuracy, and efficiency of the NSDAHU method are verified using Monte Carlo simulation method (MCSM). The NSDAHU method can not only solve hybrid random interval problems but also solve single interval, single random, and linear structural dynamics problems. [ABSTRACT FROM AUTHOR]

In this paper, the problem of a fully actuated hexarotor performing a physical interaction with the environment through a rigidly attached tool is considered. A nonlinear model predictive impedance control (NMPIC) method is proposed to achieve the goal in which the controller is able to simultaneously handle the constraints and maintain the compliant behavior. The design of NMPIC is the combination of a nonlinear model predictive control and impedance control based on the dynamics of the system. A disturbance observer is exploited to estimate the external wrench and then provide compensation for the model which was employed in the controller. Moreover, a weight adaptive strategy is proposed to perform the online tuning of the weighting matrix of the cost function within the optimal problem of NMPIC to improve the performance and stability. The effectiveness and advantages of the proposed method are validated by several simulations in different scenarios compared with the general impedance controller. The results also indicate that the proposed method opens a novel way for interaction force regulation. [ABSTRACT FROM AUTHOR]

Insight problems are likely to trigger an initial, incorrect mental representation, which needs to be restructured in order to find the solution. Despite the widespread theoretical assumption that this restructuring process happens suddenly, leading to the typical "Aha!" experience, the evidence is inconclusive. Among the reasons for this lack of clarity is that many measures of insight rely solely on the solvers' subjective experience of the solution process. In our previous paper, we used matchstick arithmetic problems to demonstrate that it is possible to objectively trace problem-solving processes by combining eye movements with new analytical and statistical approaches. Specifically, we divided the problem-solving process into ten (relative) temporal phases to better capture possible small changes in problem representation. Here, we go a step further to demonstrate that classical statistical procedures, such as ANOVA, cannot capture sudden representational change processes, which are typical for insight problems. Only nonlinear statistical models, such as generalized additive (mixed) models (GAMs) and change points analysis, correctly identified the abrupt representational change. Additionally, we demonstrate that explicit hints reorient participants' focus in a qualitatively different manner, changing the dynamics of restructuring in insight problem solving. While insight problems may indeed require a sudden restructuring of the initial mental representation, more sophisticated analytical and statistical approaches are necessary to uncover their true nature. [ABSTRACT FROM AUTHOR]

The identification of nonlinear terms existing in the dynamic model of real-world mechanical systems such as robotic manipulators is a challenging modeling problem. The main aim of this research is not only to identify the unknown parameters of the nonlinear terms but also to verify their existence in the model. Generally, if the structure of the model is provided, the parameters of the nonlinear terms can be identified using different numerical approaches or evolutionary algorithms. However, finding a non-zero coefficient does not guarantee the existence of the nonlinear term or vice versa. Therefore, in this study, a meticulous investigation and statistical verification are carried out to ensure the reliability of the identification process. First, the simulation data are generated using the white-box model of a direct current motor that includes some of the nonlinear terms. Second, the particle swarm optimization (PSO) algorithm is applied to identify the unknown parameters of the model among many possible configurations. Then, to evaluate the results of the algorithm, statistical hypothesis and confidence interval tests are implemented. Finally, the reliability of the PSO algorithm is investigated using experimental data acquired from the UR5 manipulator. To compare the results of the PSO algorithm, the nonlinear least squares errors (NLSE) estimation algorithm is applied to identify the unknown parameters of the nonlinear models. The result shows that the PSO algorithm has higher identification accuracy than the NLSE estimation algorithm, and the model with identified parameters using the PSO algorithm accurately calculates the output torques of the joints of the manipulator. [ABSTRACT FROM AUTHOR]

The article is aimed at the mathematical and optimization modeling of technological processes of surface treatments, specifically the zincing process. In surface engineering, it is necessary to eliminate the risk that the resulting product quality will not be in line with the reliability requirements or needs of customers. To date, a number of research studies deal with the applications of mathematical modeling and optimization methods to control technological processes and eliminate uncertainties in the technological response variables. The situation is somewhat different with the acid zinc plating process, and we perceive their lack more. This article reacts to the specific requirements from practice for the prescribed thickness and quality of the zinc layer deposited in the acid electrolyte, which stimulated our interest in creating a statistical nonlinear model predicting the thickness of the resulting zinc coating (ZC). The determination of optimal process conditions for acid galvanizing is a complex problem; therefore, we propose an effective solving strategy based on the (i) experiment performed by using the design of experiments (DOE) approach; (ii) exploratory and confirmatory statistical analysis of experimentally obtained data; (iii) nonlinear regression model development; (iv) implementation of nonlinear programming (NLP) methods by the usage of MATLAB toolboxes. The main goal is achieved—regression model for eight input variables, including their interactions, is developed (the coefficient of determination reaches the value of R2 = 0.959403); the optimal values of the factors acting during the zincing process to achieve the maximum thickness of the resulting protective zinc layer (the achieved optimum value th* = 12.7036 μm), are determined. [ABSTRACT FROM AUTHOR]

Perovskite materials have a variety of crystal structures, and the properties of crystalline materials are greatly influenced by geometric information such as the space group, crystal system, and lattice constant. It used to be mostly obtained using calculations based on density functional theory (DFT) and experimental data from X-ray diffraction (XRD) curve fitting. These two techniques cannot be utilized to identify materials on a wide scale in businesses since they require expensive equipment and take a lot of time. Machine learning (ML), which is based on big data statistics and nonlinear modeling, has advanced significantly in recent years and is now capable of swiftly and reliably predicting the structures of materials with known chemical ratios based on a few key material-specific factors. A dataset encompassing 1647 perovskite compounds in seven crystal systems was obtained from the Materials Project database for this study, which used the ABX3 perovskite system as its research object. A descriptor called the bond-valence vector sum (BVVS) is presented to describe the intricate geometry of perovskites in addition to information on the usual chemical composition of the elements. Additionally, a model for the automatic identification of perovskite structures was built through a comparison of various ML techniques. It is possible to identify the space group and crystal system using just a small dataset of 10 feature descriptors. The highest accuracy is 0.955 and 0.974, and the highest correlation coefficient (R2) value of the lattice constant can reach 0.887, making this a quick and efficient method for determining the crystal structure. [ABSTRACT FROM AUTHOR]

The self-similar gravitational collapse solutions to the Einstein-axion–dilaton system have already been discovered. Those solutions become invariants after combining the spacetime dilation with the transformations of internal SL(2, R). We apply nonlinear statistical models to estimate the functions that appear in the physics of Black Holes of the axion–dilaton system in four dimensions. These statistical models include parametric polynomial regression, nonparametric kernel regression and semi-parametric local polynomial regression models. Through various numerical studies, we reached accurate numerical and closed-form continuously differentiable estimates for the functions appearing in the metric and equations of motion. [ABSTRACT FROM AUTHOR]

Concrete is one of the most commonly used construction materials in the world due to its versatility. There are different types of concrete according to the required mechanical responses, and these will depend on the composition of the elements. Therefore, additional elements have been developed to improve the properties and conditions of concrete. One of these elements is reinforcing fibers made of steel, polypropylene, glass, and so on, which, according to the base material, geometry, and dosage, improve the mechanical and workability properties and decrease and/or prevent the generation of cracks, which are some of the most common problems in industrial slabs. This study performs an analysis of the changes in the mechanical properties of concrete (compressive strength, rupture modulus, modulus of elasticity, Poisson's ratio, and residual stress) due to the addition of fiber-reinforced concrete (FRC) to determine the physical and mechanical conditions of the fibers that improve the concrete and its application in industrial concrete. Due to the large number of samples and variables, advanced statistical methods (analysis of variance and comparative index) were used in the numerical study, which allowed to analyze and compare several results at the same time. This research is divided into two stages. In the first stage, six steel fibers (with a dosage of 2.7, 6, and 11 and three of 28 kg/m3) and five polypropylene fibers (with a dosage of 0.6, 2.15, and 2.7 and two of 3 kg/m3) were used in the study, and compression and bending tests (ASTM C39 and C78, respectively) were performed on 35 cylinders and 45 beams. Improvements were identified in several fiber-reinforced concrete samples in terms of compressive strength: 67% of the steel fiber samples and 100% of the polypropylene fiber samples had values above the average value of the simple concrete; in terms of the modulus of rupture, 83% of the steel fiber samples and 80% of the polypropylene fiber samples had values above the average value of the simple concrete. In the second stage, one type of steel fiber and one type of polypropylene fiber were selected for a second mechanical analysis (64 cylinders, 72 beams, and 15 slabs) with dosages of 20, 30, and 40 kg/m3 and 2.13, 4.25, and 6.38 kg/m3, respectively. In the second stage, statistical analysis and modeling with nonlinear analysis were used to evaluate the results, where residual strength improved but Poisson's ratio decreased when the dosage of fibers was increased. [ABSTRACT FROM AUTHOR]

Click beetles (Coleoptera: Elateridae) are known for their unique clicking mechanism that generates a powerful legless jump. From an inverted position, click beetles jump by rapidly accelerating their center of mass (COM) upwards. Prior studies on the click beetle jump have focused on relatively small species (body length ranging from 7 to 24 mm) and have assumed that the COM follows a ballistics trajectory during the airborne phase. In this study, we record the jump and the morphology of 38 specimens from diverse click beetle genera (body length varying from 7 to 37 mm) to investigate how body length and jumping performance scale across the mass range. The experimental results are used to test the ballistics motion assumption. We derive the first morphometric scaling laws for click beetles and provide evidence that the click beetle body scales isometrically with increasing body mass. Linear and nonlinear statistical models are developed to study the jumping kinematics. Modeling results show that mass is not a predictor of jump height, take-off angle, velocity at take-off, and maximum acceleration. The ballistics motion assumption is strongly supported. This work provides a modeling framework to reconstruct complete morphological data sets and predict the jumping performance of click beetles from various shapes and sizes. [ABSTRACT FROM AUTHOR]

In this paper, we have compared some linear and nonlinear models for explaining and forecasting the productionof two different crops, Mango and the Sugarcane using area of fields as the auxiliary variable. The models under considerations are compared on the basis of different fitting measures such as, Coefficient of Determination (R²),Residual Mean Square (s²), Mean Absolute Error (MAE) and Akaike Information Criterion (A.I.C.). The two primary data sets have been collected, first for sugarcane production from the Sitapur district of Uttar Pradesh state in India and second for mango production from the Lucknow district of Uttar Pradesh state in India. The fitting measures are calculated for the collected primary data sets and the best fitted models are selected and recommended for further use on the basis of the fitting measures. From the data analysis for both the data sets, it is found that the Compound, Growth, Exponential and Logistic models are equally good for practical applications. [ABSTRACT FROM AUTHOR]

Optimal design ideas are increasingly used in different disciplines to rein in experimental costs. Given a nonlinear statistical model and a design criterion, optimal designs determine the number of experimental points to observe the responses, the design points and the number of replications at each design point. Currently, there are very few free and effective computing tools for finding different types of optimal designs for a general nonlinear model, especially when the criterion is not differentiable. We introduce an R package ICAOD to find various types of optimal designs and they include locally, minimax and Bayesian optimal designs for different nonlinear statistical models. Our main computational tool is a novel metaheuristic algorithm called imperialist competitive algorithm (ICA) and inspired by socio-political behavior of humans and colonialism. We demonstrate its capability and effectiveness using several applications. The package also includes several theory-based tools to assess optimality of a generated design when the criterion is a convex function of the design. [ABSTRACT FROM AUTHOR]

This paper presents a predictive control strategy based on nonlinear model predictive control (NMPC) for a supercavitating vehicle system with high coupling nonlinearity and model uncertainty. By considering the cavity memory effect and introducing a cavity axis correction model, a nonlinear time-varying dynamic model of the vehicle is established to derive the time-varying tail efficiency and nonlinear planing force models. Based on the model, a predictive controller based on the nonlinear characteristics of the vehicle is designed. The controller can solve a finite horizon optical control problem (FHOCP) online for the updated system state in each system iteration to find the optimal solution at a given time. In addition, the controller introduces a Lyapunov terminal cost function and terminal constraint for the system state to reduce the system's conservativeness, which can improve the control performance of the system. The simulation results verify that the controller exhibits good stability and robustness against external interference and parameter perturbation. • An improved time-varying tail fin model is derived and established to replace constant tail fin efficiency. • Nonlinear model predictive control (NMPC) was introduced into the vehicle pitch motion control. The control algorithm can solve the finite time domain optimization control problem (FHOCP) online for the updated vehicle state in each system iteration, so as to directly deal with the nonlinear and time-varying terms of the vehicle. • In order to reduce the conservatism of the system, a terminal cost function based on Lyapunov theory was introduced into the quadratic cost function. [ABSTRACT FROM AUTHOR]

• Combining CBPT with GLMEs allows statistical analysis to match experimental design. • CBPT with GLMEs accounts for subject variability and hierarchical random effects. • The proposed method maintains control of type I error, type II error, and FWER. • CBPT with GLMEs can be applied to individual channels and pseudo-population data. Time series analysis is critical for understanding brain signals and their relationship to behavior and cognition. Cluster-based permutation tests (CBPT) are commonly used to analyze a variety of electrophysiological signals including EEG, MEG, ECoG, and sEEG data without a priori assumptions about specific temporal effects. However, two major limitations of CBPT include the inability to directly analyze experiments with multiple fixed effects and the inability to account for random effects (e.g. variability across subjects). Here, we propose a flexible multi-step hypothesis testing strategy using CBPT with Linear Mixed Effects Models (LMEs) and Generalized Linear Mixed Effects Models (GLMEs) that can be applied to a wide range of experimental designs and data types. We first evaluate the statistical robustness of LMEs and GLMEs using simulated data distributions. Second, we apply a multi-step hypothesis testing strategy to analyze ERPs and broadband power signals extracted from human ECoG recordings collected during a simple image viewing experiment with image category and novelty as fixed effects. Third, we assess the statistical power differences between analyzing signals with CBPT using LMEs compared to CBPT using separate t-tests run on each fixed effect through simulations that emulate broadband power signals. Finally, we apply CBPT using GLMEs to high-gamma burst data to demonstrate the extension of the proposed method to the analysis of nonlinear data. First, we found that LMEs and GLMEs are robust statistical models. In simple simulations LMEs produced highly congruent results with other appropriately applied linear statistical models, but LMEs outperformed many linear statistical models in the analysis of "suboptimal" data and maintained power better than analyzing individual fixed effects with separate t-tests. GLMEs also performed similarly to other nonlinear statistical models. Second, in real world human ECoG data, LMEs performed at least as well as separate t-tests when applied to predefined time windows or when used in conjunction with CBPT. Additionally, fixed effects time courses extracted with CBPT using LMEs from group-level models of pseudo-populations replicated latency effects found in individual category-selective channels. Third, analysis of simulated broadband power signals demonstrated that CBPT using LMEs was superior to CBPT using separate t-tests in identifying time windows with significant fixed effects especially for small effect sizes. Lastly, the analysis of high-gamma burst data using CBPT with GLMEs produced results consistent with CBPT using LMEs applied to broadband power data. We propose a general approach for statistical analysis of electrophysiological data using CBPT in conjunction with LMEs and GLMEs. We demonstrate that this method is robust for experiments with multiple fixed effects and applicable to the analysis of linear and nonlinear data. Our methodology maximizes the statistical power available in a dataset across multiple experimental variables while accounting for hierarchical random effects and controlling FWER across fixed effects. This approach substantially improves power leading to better reproducibility. Additionally, CBPT using LMEs and GLMEs can be used to analyze individual channels or pseudo-population data for the comparison of functional or anatomical groups of data. [ABSTRACT FROM AUTHOR]

This paper investigates the nonlinear model predictive control to automatic train regulation in metro systems under dynamically disturbed environments. A coupled state-space model for the evolution of train traffic and passenger load dynamics is explicitly developed. With the safety constraints, a mixed-integer nonlinear optimal control model is formulated to generate train regulation strategies. An efficient solution algorithm, based on spatial branch and bound combined with generalized Benders decomposition, is particularly designed to obtain exact solutions quickly for the embedded applications. The original complex problem can be converted into several smaller convex quadratic subproblems with reduced domains to be solved quickly. The effectiveness of our train control model and approaches are verified in numerical simulations. Computational results illustrate that the proposed control method effectively improves the operational efficiency and meanwhile reduces energy consumption during operations. Besides, the efficient embedded solution algorithm performs satisfactory computational efficiency, facilitating a real-time implementation. [ABSTRACT FROM AUTHOR]

Many problems in computational materials science and chemistry require the evaluation of expensive functions with locally rapid changes, such as the turn-over frequency of first principles kinetic Monte Carlo models for heterogeneous catalysis. Because of the high computational cost, it is often desirable to replace the original with a surrogate model, e.g., for use in coupled multiscale simulations. The construction of surrogates becomes particularly challenging in high-dimensions. Here, we present a novel version of the modified Shepard interpolation method which can overcome the curse of dimensionality for such functions to give faithful reconstructions even from very modest numbers of function evaluations. The introduction of local metrics allows us to take advantage of the fact that, on a local scale, rapid variation often occurs only across a small number of directions. Furthermore, we use local error estimates to weigh different local approximations, which helps avoid artificial oscillations. Finally, we test our approach on a number of challenging analytic functions as well as a realistic kinetic Monte Carlo model. Our method not only outperforms existing isotropic metric Shepard methods but also state-of-the-art Gaussian process regression. [ABSTRACT FROM AUTHOR]

Conventional statistical models provide inaccurate predictions of concrete crack openings because they do not consider the nonlinear temperature response and the residual characteristics of concrete. To address this problem, this study introduces a nonlinear temperature factor and develops an improved statistical model of crack openings. The chaotic characteristics of residual time series of the improved statistical model are analyzed based on chaos theory and phase-space reconstruction theory. These theories are integrated with back-propagation (BP) artificial neural networks and genetic algorithms (GAs) to establish a GA-BP neural network model for predicting residuals. Finally, a hybrid model is developed for predicting the concrete crack opening behavior. The predictions of the conventional statistical model, the statistical model considering nonlinear temperature component, and the hybrid model are compared using the case study on the crack openings of a regulating sluice. The results show that the proposed hybrid model in this study for predicting concrete crack openings is significantly more accurate than the conventional statistical model and the statistical model considering nonlinear temperature component. [ABSTRACT FROM AUTHOR]

The article aimed to outline a statistical model based on nonlinear regressions for the efficiency of the Fluorescence Resonance Energy Transfer (FRET) phenomenon and to select a general simulation method of the FRET phenomenon, independent of the nature of the active substance used as a donor. For this purpose, several statistical models of Polynoal and nonlinear Gaussiene regressions were first tested, which would fit as well as possible the variation in time efficiency and its dependence on the concentrations of active substances. The regression models used were implemented and tested in the R language. In the second part of the study we aprooached the problem of simulating the efficiency of FRET by adapting a Monte Carlo method, using as a starting point the previously determined regressive models. [ABSTRACT FROM AUTHOR]

Wireless ranging measurements have been proposed for enabling multiple Micro Air Vehicles (MAVs) to localize with respect to each other. However, the high-dimensional relative states are weakly observable due to the scalar distance measurement. Hence, the MAVs have degraded relative localization and control performance under unobservable conditions as can be deduced by the Lie derivatives. This paper presents a nonlinear model predictive control (NMPC) by maximizing the determinant of the observability matrix to generate optimal control inputs, which also satisfy constraints including multi-robot tasks, input limitation, and state bounds. Simulation results validate the localization and control efficacy of the proposed MPC method for range-based multi-MAV systems with weak observability, which has faster convergence time and more accurate localization compared to previously proposed random motions. A real-world experiment on two Crazyflies indicates the optimal states and control behaviours generated by the proposed NMPC. [ABSTRACT FROM AUTHOR]

The work presented in this paper investigates the possibility of using statistical linearization approach to predict the stochastic dynamics of nonlinear floating offshore wind turbines (FOWT) system under coupled nonlinear wave excitation. OC3-Hywind Spar floating platform equipped with a 5 MW reference wind turbine offered by NREL is taken as the discussed FOWT of the study. A coupled nonlinear analytical model subjected to coupled irregular wave excitation is established to describe the dynamic characteristics of the FOWT, and further, the governing equation of the model is transformed into the equivalent linear one through the statistical linearization approach. The error caused in the linearization process is minimized through an iterative process. The accuracy, adaptability and efficiency of the model and the proposed method built in this paper are verified by numerical examples and comparison with other methods, such as Monte Carlo method and FAST etc. The research shows that the proposed method based on statistical linearization process to predict the stochastic dynamic response of nonlinear FOWT system under coupled nonlinear wave excitation has not only high accuracy but also high computational efficiency. It is about 4–5 orders of magnitude faster than the deterministic analysis method, and is of great significance for the design and research of the FOWT. An application example conducted in this paper indicates that this method is effective and can be used extensively, such as structural design optimization, reliability analysis, determination of the optimal parameters of vibration control and fatigue analysis of components, etc. • Coupled nonlinear analytical model to describe the dynamic characteristics of the floating offshore wind turbine. • Consideration of both system nonlinearity and hydrodynamic nonlinearity. • Application of the statistical linearization, and determination of an equivalent linear system. • Solution of the stochastic response via frequency domain method. • A sensitivity study is performed to investigate the second-order motion under different sea conditions. • Nonlinear model shows better agreement with FAST compare to linear model, especially in extreme sea conditions. [ABSTRACT FROM AUTHOR]

Objectives: Morphology of polar sea ice changes continuously under the influences of external driving forces, e.g., wind, flow and waves. Observations and change regularity analysis on morphology of polar sea ice not only is helpful for the optimization of sea ice thermodynamics numerical simulation and the inversion of sea ice thickness based on roughness parameters, but also provide distinct importance for under⁃ standing the response of polar sea ice morphology to climate change. Methods: Based on the undulations profiles of sea ice bottom in Northwestern Weddell Sea measured by the helicopter⁃borne electromagnetic⁃ induction bird (EM⁃bird) system during the winter of 2006, this paper establishes a nonlinear statistical optimal model with the cutoff draft as the identified variable to separate the ridge keels and local undulations on sea ice bottom. Then, the keel morphology parameters and correlation between the keel draft and frequency are statistically analyzed. Finally, a new parameter T is hatched to assess the correlation between the ridge sail height and keel draft.Results: The results suggest that the optimal cutoff draft is 3.8 m and the keel spacing is the main factor affecting the ridging intensity. Despite marked sea ice deformation in different regions in Northwestern Weddell Sea, morphology changes of the keel are not evident. There is a good log correlation between draft and frequency of the keel, which vividly describes morphology and distribution of the keel. The newly hatched parameter T has a significant linear correlation with keel depth (r=0.93). Conclusions: The newly proposed modeling method can separate the keel from sea ice bottom undulations more accurately, which provides a new theoretical reference for morphological correlation analysis between surface and bottom of sea ice, and the inversion of sea ice thickness and bottom draft from its surface height. [ABSTRACT FROM AUTHOR]

Investigations have shown that in the local approximation (for sizes L < 100 µm), AlGaN/GaN high electron mobility transistor (HEMT) structures satisfy to all properties of chaotic systems and can be described in the language of fractal geometry of fractional dimensions. For such objects, values of their electrophysical characteristics depend on the linear sizes of the examined regions, which explain the presence of the so-called size effects—dependences of the electrophysical and instrumental characteristics on the linear sizes of the active elements of semiconductor devices. In the present work, a relationship has been established for the linear model parameters of the equivalent circuit elements of internal transistors with fractal geometry of the heteroepitaxial structure manifested through a dependence of its relative electrophysical characteristics on the linear sizes of the examined surface areas. For the HEMTs, this implies dependences of their relative static (A/mm, mA/V/mm, Ω/mm, etc.) and microwave characteristics (W/mm) on the width d of the sink-source channel and on the number of sections n that leads to a nonlinear dependence of the retrieved parameter values of equivalent circuit elements of linear internal transistor models on n and d. Thus, it has been demonstrated that the size effects in semiconductors determined by the fractal geometry must be taken into account when investigating the properties of semiconductor objects on the levels less than the local approximation limit and designing and manufacturing field effect transistors. In general, the suggested approach allows a complex of problems to be solved on designing, optimizing, and retrieving the parameters of equivalent circuits of linear and nonlinear models of not only field effect transistors but also any arbitrary semiconductor devices with nonlinear instrumental characteristics. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to focus on studying the influence of gradient parameters, namely, thickness coefficient, length coefficient and height ratio of auxetic structure on responses such as strength, stiffness and specific energy absorption (SEA) under compressive loading. Optimization of significant parameters is also performed to maximize responses. Further, efforts have also been made to develop regression models for strength, stiffness and SEA of auxetic structure. Design/methodology/approach: Central composite design of response surface methodology is used for planning experiments. Auxetic structures of acrylonitrile butadiene styrene (ABS) and poly-lactic acid (PLA) materials are fabricated by the material extrusion (ME) technique of additive manufacturing. Fabricated structures are tested under in-plane uniaxial compressive loading. Grey relational analysis is used for the optimization of gradient parameters of the unit cell of auxetic structure to maximize responses and minimize weight and time of fabrication. Findings: From the analysis of variance of experimental data, it is found that the compressive strength of auxetic structures increases with a decrease in length coefficient and height ratio. In the case of ABS structures, stiffness increases with a decrease in thickness coefficient and length coefficient, while in the case of PLA structures, stiffness increases with a decrease in length coefficient and height ratio. SEA is influenced by length coefficient and thickness coefficient in ABS and PLA structures, respectively. Based on the analysis, statistical non-linear quadratic models are developed to predict strength, stiffness and SEA. Optimal configuration of auxetic structure is determined to maximize strength, stiffness, SEA and minimize weight and time of fabrication. Research limitations/implications: The present study is limited to re-entrant type of auxetic structures made of ABS and PLA materials only under compressive loading. Also, results from the current study are valid within a selected range of gradient parameters. The findings of the present study are useful in the optimal selection of gradient parameters for the fabrication of auxetic structures of maximum strength, stiffness and SEA with minimum weight and time of fabrication. These outcomes have wide applications in domains such as automotive, aerospace, sports and marine sectors. Originality/value: Limited literature is available on studying the influence of gradient parameters of ME manufactured auxetic structure of ABS and PLA materials on responses, namely, strength, stiffness and SEA under compressive loading. Also, no work has been reported on studying the influence of gradient parameters on mechanical properties, weight and time of fabrication of auxetic structures. The present study is an attempt to fulfil the above research gaps. [ABSTRACT FROM AUTHOR]

Objective: This study was focused on the estimation of parameters of Wood’s model and description of the lactation curve using the cows which were lactated over 24 months on the first lactation. Methods: The database included 1,333 pure-bred dairy Simmental primiparous cows which lactated for 24 months (732 days). The initial dataset entering the procedure of assessment of parameters of Wood’s function included 35,826 milk yield records. Milk yield was recorded throughout lactation, with the earliest record taken on day 6 and the latest on day 1,348 of lactation. This dataset was used for the assessment of parameters a, b, c of Wood’s model using the non-linear statistical procedure. These parameters were estimated for different length of lactation. The assessed parameters were used for calculation of some characteristics of lactation curves. Results: The lowest value of a parameter (15.2317) of Wood’s model of lactation curve was found out in lactations up to 305 days long, contrary to b and c parameters which were highest in those lactations (0.1029 and 0.0015, respectively). The maximum value of a parameter (17.4329) was found out in lactations up to 640 days long, unlike b and c parameters which were minimal in those lactations (0.0603 and 0.0010, respectively). Conclusion: It can be concluded that the parameters of Wood’s model and the shape of lactation curve are changing with the growing number of milk yield records. Also, the assessed parameters revealed a significant milk production potential after 305 days of lactation. [ABSTRACT FROM AUTHOR]

Nonlinear dielectric measurements at high electric fields of glass-forming glycerol and propylene carbonate initially were carried out to elucidate the dynamic heterogeneous nature of the structural α-relaxation. Recently, the measurements were extended to sufficiently high frequencies to investigate the nonlinear dielectric response of faster processes including the so-called excess wing (EW), appearing as a second power law at high frequencies in the loss spectra of many glass formers without a resolved secondary relaxation. While a strong increase of dielectric constant and loss is found in the nonlinear dielectric response of the α-relaxation, there is a lack of significant change in the EW. A surprise to the experimentalists finding it, this difference in the nonlinear dielectric properties between the EW and the α-relaxation is explained in the framework of the coupling model by identifying the EW investigated with the nearly constant loss (NCL) of caged molecules, originating from the anharmonicity of the intermolecular potential. The NCL is terminated at longer times (lower frequencies) by the onset of the primitive relaxation, which is followed sequentially by relaxation processes involving increasing number of molecules until the terminal Kohlrausch α-relaxation is reached. These intermediate faster relaxations, combined to form the so-called Johari-Goldstein (JG) β-relaxation, are spatially and dynamically heterogeneous, and hence exhibit nonlinear dielectric effects, as found in glycerol and propylene carbonate, where the JG β-relaxation is not resolved and in D-sorbitol where it is resolved. Like the linear susceptibility, X1(ƒ), the frequency dispersion of the third-order dielectric susceptibility, X3(ƒ), was found to depend primarily on the α-relaxation time, and independent of temperature T and pressure P. I show this property of the frequency dispersions of X1(ƒ) and X3(ƒ) is the characteristic of the many-body relaxation dynamics of interacting systems which are governed solely by the intermolecular potential, and thermodynamic condition plays no role in this respect. Although linked to X3(ƒ), dynamic heterogeneity is one of the parallel consequences of the many-body dynamics, and it should not be considered as the principal control parameter for the other dynamic properties of glassforming systems. Results same as X3(ƒ) at elevated pressures had been obtained before by molecular dynamics simulations from the four-points correlation function and the intermediate scattering function. Naturally all properties obtained from the computer experiment, including dynamics heterogeneity, frequency dispersion, the relation between the α- and JG β-relaxation, and the breakdown of the Stokes-Einstein relation, are parallel consequences of the many-body relaxation dynamics governed by the intermolecular potential. [ABSTRACT FROM AUTHOR]

To more accurately describe the noncontact transport behavior of traveling acoustic waves, a nonlinear model is presented in this paper for the squeeze gas film with consideration of gas inertia in the case of a large amplitude motion and low viscosity of the gas. A closed form solution is derived for the vertical and horizontal forces of the film from this model. Our results have shown that the gas inertia has a significant influence on the pressure distribution in the squeeze film, and the inertial force is higher than the viscous force. The predicted levitation and horizontal driving forces are found to be in good agreement with our experimental measurements. Our inertia model provides a powerful tool for the force estimation and its potential benefits could be far reaching. The accurate prediction of these forces is useful to design the system for levitating and transporting planar objects, such as MEMS devices, glass substrates, and IC chips. [ABSTRACT FROM AUTHOR]

And the time-series techniques that developmental scientists employ in analyzing these data sets need to be geared toward embracing the nonlinear complexity of temporal dynamics, utilizing advanced modeling techniques that are now readily available (Molenaar & Campbell, 2009; Molenaar & Newell, 2010). Keywords: Relational developmental systems; Intraindividual variability; Person-centered framework; Methodology; Process; Metatheory EN Relational developmental systems Intraindividual variability Person-centered framework Methodology Process Metatheory 19 22 4 04/24/21 20210101 NES 210101 In their illuminating work, Budwig and Alexander (this issue, DOI 10.1159/000514553) address the question of metatheoretical agreement between current scholarship in developmental science and current practice in higher education, both inside and outside the classroom. Overton (2015a) has emphasized the importance of "ensur[ing] that process is the center of attention, and part and parcel of the methodology of developmental science" (p. 33). [Extracted from the article]

Controlling traffic in large-scale urban road networks is a challenging problem. Aggregated dynamical models, based on the macroscopic fundamental diagram (MFD) of urban traffic, enable model-based control design. As an alternative to perimeter control actuation commonly used in MFD-based control, in this paper, we propose actuation over regional space-mean speeds, which we name average speed control. The method involves manipulation of regional speeds via instrumentation similar to variable speed limits in freeways, or using vehicle-to-infrastructure communication. We develop nonlinear model predictive control schemes considering actuation over average speed and perimeter control. Their performances are compared using simulations on congested scenarios, the results of which suggest potential of the method as an alternative or complementary actuation to perimeter control. • A novel speed control strategy for urban networks. • Manipulation of regional average speeds to optimize traffic conditions in the whole network. • An MPC scheme combining perimeter control and average speed control. [ABSTRACT FROM AUTHOR]

This paper investigates the first and second-order stochastic responses of oscillating water columns (OWCs) under random waves. The OWCs' nonlinear dynamics are computed in the frequency domain, where sources of nonlinearities are replaced by equivalent polynomial terms up to second order by minimising their difference in a mean-square sense. This procedure is known as the statistical quadratisation (SQ) technique. In such an approach, the linear and quadratic coefficients are obtained using an iterative procedure and non-Gaussian distributions based on Gram–Charlier expansions, and the dynamics are solved using the Volterra theory. The results are compared against a statistical linearisation model (SL), and nonlinear time-domain simulations (TD) to illustrate the capabilities of the method. The result demonstrated an excellent agreement for the first and second-order motions of the water column obtained using statistical quadratisation compared to nonlinear time-domain simulations in terms of spectral response and probability distribution. Along with the good accuracy, the statistical quadratisation has the advantage of being approximately two orders of magnitude faster than nonlinear time-domain simulations. For the proposed systems, the nonlinearity from the variable mass system (inertial type) is shown to be the most important source of second-order effects driving the oscillating water column dynamics based on the environmental conditions and drafts investigated in this work. [ABSTRACT FROM AUTHOR]

A contentious issue regarding immigration is the contempt that many natives hold for immigrants. Studies report that enhanced job competition, the potential for cultural misunderstandings, and security fears are salient in predicting native population discontent for immigrants. However, the threat posed by terrorism has been overlooked in prior research. Using data drawn from twenty mostly European countries and a nonlinear hierarchical modeling statistical procedure, we find that acts of terrorism are associated with a substantive decrease in the desire for outgroup immigration. No effect of terrorism on ingroup immigration is observed. The theoretical implications of these findings are discussed. [ABSTRACT FROM AUTHOR]

Climatic conditions easily allow for volume change of expansive soils. This happens due to the wetting and drying cycles that affect the moisture active zone. The suction compression index (γh) is the key parameter that relates volumetric changes to soil suction changes in unsaturated soils. It is defined as the slope of the suction–volumetric strain relationship. This relationship is essentially nonlinear and the slope is determined for an idealized form of the relationship. Therefore, the need for a precise γh determination method has always been in demand. The more accurate γh is determined, the more accurate soil movements can be predicted and taken care of early in the design stage of a project. Accordingly, more money can be saved from either the repair costs or the initial costs by avoiding overdesign. The purpose of this paper is to introduce an original testing method for γh determination. The testing method uniquely incorporates volumetric and suction measurements in a new and practical way utilizing simple digital imaging. The testing method unprecedentedly integrates statistical modeling for determination of incremental γh in order to cover the entire nonlinearity of the suction–volumetric strain relationship. This is done by fitting the S-shaped relationship by a well-known class of statistical functions called cumulative distribution functions (CDFs). Incremental γh is estimated by estimating the CDF at every suction value. The appropriateness of using these estimates to describe the suction–volumetric strain relationship is evaluated using the Kolmogorov–Smirnov (K–S) goodness of fit test. Furthermore, 95% confidence intervals of the superposed curves are also used to assess the appropriateness of the CDF estimates. The new testing method is compared against other techniques in the literature and proven reliable results. [ABSTRACT FROM AUTHOR]

Expensive black box systems arise in many engineering applications but can be difficult to optimize because their output functions may be complex, multi-modal, and difficult to understand. The task becomes even more challenging when the optimization is subject to multiple constraints and no derivative information is available. In this article, we combine response surface modeling and filter methods in order to solve problems of this nature. In employing a filter algorithm for solving constrained optimization problems, we establish a novel probabilistic metric for guiding the filter. Overall, this hybridization of statistical modeling and nonlinear programming efficiently utilizes both global and local search in order to quickly converge to a global solution to the constrained optimization problem. To demonstrate the effectiveness of the proposed methods, we perform numerical tests on a synthetic test problem, a problem from the literature, and a real-world hydrology computer experiment optimization problem. [ABSTRACT FROM AUTHOR]

An optimal predictive control algorithm is introduced for the control of linear and nonlinear discrete-time multivariable systems. The controller is specified in a 'restricted structure' form involving a set of given linear transfer-functions and a set of gains that minimise a Generalised Predictive Control (GPC) cost-index. The set of functions can be chosen as proportional, integral and derivative terms, however, a wide range of controller structures is possible. This is referred to as Restricted-Structure GPC control. The multi-step predictive control cost-function is novel, since it includes weightings on the 'low-order' controller gains and the rate of change of gains. This considerably improves the numerical computations ensuring critical inverse computations cannot lead to a singular matrix. It also provides the option of adding soft or hard constraints on the controller gains which provides additional flexibility for control design. The ability to include a plant model that can include a general nonlinear operator is also new for restricted structure control solutions. The low-order controller provides a potential improvement in robustness, since it is often less sensitive to plant uncertainties. The simple controller structure also enables relatively unskilled staff to retune the system using familiar tuning terms, and provides a potentially simpler QP problem for the constrained case. [ABSTRACT FROM AUTHOR]

Summary: This work is concerned with the robust model predictive control (MPC) for a class of distributed networked control systems (NCSs), in which the input quantization and switching topology are both considered. By utilizing the sector bound approach, the NCSs with quantization are converted into the linear systems with sector bound uncertainties. The topology switching is governed by a switching signal and the dynamic behavior is modeled as a switched control system. A new robust MPC design technique is derived to minimize the upper bound of a weighted quadratic performance index. Moreover, the conditions of both the recursive feasibility of the MPC design and the stability of the resulting closed‐loop system are developed. Finally, simulation results are presented to verify the effectiveness of the proposed MPC design. [ABSTRACT FROM AUTHOR]

Electrochemical strain microscopy (ESM) is a powerful tool to resolve ionic transport and electrochemical processes with a nanoscale resolution. To ascertain the underlying mechanism that governs the signal generation of ESM imaging, a fully coupled nonlinear electrochemomechanical model based on the finite element method is developed and applied to LiMn2O4 particles. The frequency dependence of the ESM response, in particular the response at high frequencies used in the detection regime, is investigated in detail. The performed analysis demonstrates that the error induced by the decoupling approximation increases with decreasing bias frequency due to the relatively large variation in ion concentration. In the high frequency regime, the results reveal that the stress effect is negligible and local electroneutrality holds, providing the simplification of numerical simulation for ESM imaging. By applying an alternative current voltage, we suggest that the detectable signal observed in ESM imaging can be attributed to the Vegard effect, which was controversial in previous linear models. The local distribution of ion concentration shows that the ionic reorganization only takes place near the tip–surface junction, the spatial extent of which can be described by two relevant lengths, the contact radius and ion drift length, which determine the spatial lateral resolution and depth resolution, respectively, in ESM imaging. Through a parametric study, the electromigration is proved to be dominant at high frequencies and the relationship between ESM amplitude and some parameters may offer a strategy to measure local electrochemical reactivity. The impact of contact force is evaluated and the results indicate that the local compression reduces ion concentration and the resultant ESM signal in the detection regime. Thus attention must be paid to the contact force when a comparison between different measurements is conducted. The combination of the numerical model and experiment holds the promise of quantitative probing of local electrochemical parameters in solids. [ABSTRACT FROM AUTHOR]

Functional link artificial neural network (FLANN) has received much attention due to its wide applicability. The Van der Pol-Duffing oscillator (VdPDO)-based nonlinear systems, which own complex dynamical behaviors, identification of such nonlinear model is vital. This paper exploits the nonlocality of fractional calculus, aiming to enhance the identification accuracy of the VdPDO-based nonlinear systems. The proposed combined FEM-LMS (CFEM-LMS) algorithm, which is based on the FLANN structure, convexly combines the least mean square (LMS) algorithm and the newly proposed fractional-order error modified LMS (FEM-LMS) algorithm. The CFEM-LMS algorithm has improved performance and can dynamically adapt to the nonlinearity of the system. As an added contribution, a novel mixing parameter adaptation criterion is proposed for performance improvement. Extensive simulation results in the context of VdPDO-based nonlinear system identification demonstrate the superiority of the proposed algorithm as compare to state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

This paper presents a fundamental solution method for nonlinear fractional regularized long-wave (RLW) models. Since analytical methods cannot be applied easily to solve such models, numerical or semianalytical methods have been extensively considered in the literature. In this paper, we suggest a solution method that is coupled with a kind of integral transformation, namely Elzaki transform (ET), and apply it to two different nonlinear regularized long wave equations. They play an important role to describe the propagation of unilateral weakly nonlinear and weakly distributer liquid waves. Therefore, these equations have been noticed by scientists who study waves their movements. Particularly, they have been used to model a large class of physical and engineering phenomena. In this context, this paper takes into consideration an up-to-date method and fractional operators, and aims to obtain satisfactory approximate solutions to nonlinear problems. We present this achievement, firstly, by defining the Elzaki transforms of Atangana–Baleanu fractional derivative (ABFD) and Caputo fractional derivative (CFD) and then applying them to the RLW equations. Finally, numerical outcomes giving us better approximations after only a few iterations can be easily obtained. [ABSTRACT FROM AUTHOR]

Wiener model structures have attracted considerable attentions due to their powerful modelling abilities to solve industrial problems. This paper considers a new class of Wiener model structure, which consists of a linear transfer function and a radial basis function (RBF) network. Based on this structure, the unstable zero-dynamics, the added disturbances and the approximation errors problems can be dealt with. Since the model parameters are assumed unknown, we develop a new indirect adaptive control scheme. It is a consensus that the certainty equivalence principle is the key that connects the adaptation algorithm with the control law. This paper explains that, for indirect adaptive control of Wiener model, this principle may not be applied directly. The main contribution of this paper is to address this issue by presenting a rigorous theoretical analysis. Representative examples including a simulated pH process control problem are studied to test the control performance. Comparison results indicate that the proposed controller has much wider applicability than some alternative methods, especially for its improved performance and smoother adaptation. [ABSTRACT FROM AUTHOR]