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Health parameter estimation is the core of engine gas path analysis (GPA), which is widely adopted for engine safety improvement, as well as for operation and maintenance cost reduction. The major challenge of GPA lies in the contradiction between the high dimensions of parameters under estimation, e.g., health parameters, and the limited measurements obtainable from a small number of sensors. Existent GPA methods for health parameters commonly apply dimension reduction before estimation, leading to information loss and hence inaccurate estimation. To tackle the challenge of limited sensor measurements and to have more system outputs than parameters under estimation, we proposed to augment the output vector of the system model by combining the measurements from multiple adjacent operating points. But the engine model can face the problem of homogenization if using data from adjacent operating points. This can, in turn, lead to a low identifiability of parameters. We analyze the internal mechanism of such large deviation of the parameter estimation results based on linear models and argue for the need of nonlinear method. Hence, we propose a multistage nonlinear parameter estimation method for health parameters, combining biased and unbiased estimation. In our extensive simulations based on 10 output measurements of a JT9D engine, our method can estimate 130% more parameters than the widely used GPA method, while reducing the maximum estimation error of health parameters from 2.2% to 0.1%. [ABSTRACT FROM AUTHOR]

This paper presents a simultaneous state-input-stiffness estimation framework for nonlinear systems. The technique combines an unbiased minimum variance estimator (MVE) from the data assimilation context with the Moore–Penrose pseudo-inverse. The investigation employs synthetically generated measurement data with additive Gaussian noise to replicate the field measurements. In the first stage of the algorithm, the MVE estimates a total force component, a function of the unknown system parameters and unknown input excitation. In the second stage, a system of over-determined equations is established by representing the input excitation with a Fourier series (with N coefficients) expansion. A least-squares solution for the unknown stiffness parameters and the Fourier coefficients is then achieved using the Moore–Penrose inverse. A dimensionless transformation handles the scale difference between the Fourier coefficients and unknown parameters. The novelty of the work is that the inputs and the parameters of the nonlinear systems are estimated simultaneously, circumventing any linearization of the system, and the associated computational hassles. The method by construction has a unique solution and an upper bound for stiffness estimation error is derived. The method is demonstrated numerically for Duffing oscillator systems excited by random inputs. The robustness of the technique is assessed by conducting various parametric studies. Numerical results reveal that the developed method accurately estimates the nonlinear cubic stiffness parameter, input force, and state responses. [ABSTRACT FROM AUTHOR]

Purpose: Response characteristics of nonlinear systems have been extensively studied for system identification. But all these studies mainly employ single tone harmonic excitation. In contrast, there are very few research literatures on the use of multi-tone harmonic excitation, obviously due to the challenges in more complicated formulation of response characteristics. This research intends to identify a polynomial type of damping nonlinearity using Higher-order Frequency Response Functions (HoFRFs) and harmonic amplitude measurement data under multi-tone harmonic excitation. Methods: In the present study, the Volterra series is employed to demonstrate benefits of using multi- tone harmonic excitation for identification of damping nonlinearity. It is shown a large number of combination tones of higher harmonics are formed in the response spectrum. Response harmonic amplitude series are formulated for these harmonics using higher order Volterra kernel synthesis for both symmetric and asymmetric forms of damping nonlinearity. Results and conclusion: A novel parameter estimation algorithm is presented to first estimate the nonlinear parameter and then the linear modal parameters of the system using two experiments only, whereas, for single-tone harmonic excitation, one would require at least six to eight experiments. The signal strength of higher harmonics is studied for selection of most effective frequency combinations in the multi-tone excitation. Numerical simulations with a typical two-tone excitation demonstrate that fairly accurate estimates of nonlinear damping parameters and linear modal parameters can be obtained with proper selection of frequency pair and excitation level. [ABSTRACT FROM AUTHOR]

Estimating the stiffness and damping coming from a joint is a major challenge. This work proposes a novel framework to estimate the unknown parameters using frequency-based substructuring and a maximum a posteriori optimization approach. The idea is to minimize the discrepancy between the measured responses of an assembled system and the responses obtained by coupling the measured responses of the individual substructures with a joint model. The system's dynamics are assumed to be linear. However, the FRFs of the coupled system (which drive the objective function) change nonlinearly with the linear joint parameters, making it a nonlinear optimization problem. Therefore, the joint parameters are estimated iteratively using the Gauss minimization method, a well-established linearization scheme. The framework is numerically validated on simple mdof systems, and the method's effectiveness is tested by adding noise to the generated data. To demonstrate the framework's applicability on real structures, joint stiffness and damping of a known benchmark structure are estimated based on real measurements. Two different underlying joint models are applied. The first assumes just flexibility at the connection. The second assumes the joint is a separate substructure. The algorithm can find realistic stiffness parameters and a range for the damping parameters providing a good compromise between the measurements in both cases. [ABSTRACT FROM AUTHOR]

We have previously proposed a live simulation model (LSM) that simulates the heat-balance of high-voltage overhead transmission lines during high-temperature operation without requiring meteorological parameters. This can be used to calculate the dynamic line rating (DLR) in real time, although the accuracy of this approach is significantly influenced by measurement errors in the real-time monitoring system. This paper describes how the nonlinear parameter estimation method can be combined with the tabu search algorithm to determine the optimal solutions for systematic deviation corrections in a practical application of LSM. The effects of this optimal solution on LSM are verified using data from a 110-kV transmission line. The accuracy of the deviation corrections is verified through comparisons with field measurements. After correcting the measurement errors, the average error in the real-time DLR calculated by LSM is expected to be less than 1% and the root mean-squared error is expected to be less than 10%. [ABSTRACT FROM AUTHOR]

Accurate physical modeling of vehicle dynamics requires extensive a priori knowledge of the studied vehicle. In contrast, data-driven modeling approaches require only a set of data that are a good account of the vehicle’s driving envelope. In this brief, we compare, for the first time, the prediction capabilities of both approaches applied to a large-scale real-world driving data set. The data set contains several cornering maneuvers, acceleration, and deceleration stages and was collected over public roads. Linear and nonlinear physical models were identified through nonlinear optimization of their unknown parameters. Closed-form subspace identification methods were used to initialize the estimate of a linear state-space model, and the initialization was then refined through nonlinear optimization. The optimized models were validated against 59 km of independent driving data. The model fits, in the longitudinal velocity, were 68.9% versus 80.2% for the nonlinear physical model and linear data-driven (second-order) model, respectively, and, in the yaw rate, 43.0% versus 63.5%. These results show that, for this vehicle, a simple linear data-driven model outperformed both linear and nonlinear physical models under real-world driving conditions. This has important implications for control design approaches in autonomous vehicles. [ABSTRACT FROM AUTHOR]

Precise and rapid extraction of spherical target features from laser point clouds is critical for achieving high-precision registration of multiple point clouds. Existing methods often use linear models to represent spherical target characteristics, which have several drawbacks. This paper proposes a rigorous estimation algorithm for spherical target features based on least squares configurations, in which the point-cloud data error is used as a random parameter, while the spherical center coordinates and radius are used as nonrandom parameters, emphasizing correlation between spherical parameters. The implementation details of this algorithm are illustrated by deriving calculation formulas for three variance–covariance matrices: variance–covariance matrices of the new observations, variance–covariance matrices of the new observation noise, and variance–covariance matrices of random parameters and the new observation noise. Experiments show that the estimation accuracy of sphere centers using our method is improved by at least 5.7% compared to classical algorithms, such as least squares, total least squares, and robust weighted total least squares.

A novel solution to the problem of estimating nonlinear parameters is presented. The peculiarity of this problem is that the arguments of a nonlinear function comprise not only the measurement data and required parameters, but also supplementary parameters. Although supplementary parameters are a priori unknown, they are necessary for obtaining optimal estimates of the required parameters, which is carried out according to the point-mass method as a weighted sum of partial estimates obtained for the specified values of supplementary parameters. The considered solution allows priori probabilities for supplementary parameters to be rejected by taking into account additional covariance of weighting coefficients and (or) specified partial estimates. The described approach is effective in solving specified nonlinear estimation problems characterized by low accuracy of available measurement data and (or) their few number. [ABSTRACT FROM AUTHOR]

ABSTRACT Lactation records from cows of the southwestern Paraná state, Brazil, form the dataset of this study. We applied the information-theoretic approach to evaluate the ability of the nonlinear Wood, Brody, Dijkstra, and Gamma functions to fit to these data by employing a two-step technique based on nonlinear mixed-effects models and generalized linear mixed-effects models. Wood's equation was fitted with the combination of a first-order autoregressive correlation structure and a variance function to account for heteroscedasticity. This version was the best choice to mimic lactation records. Some geometric attributes of Wood's model were deduced, mainly the ascending specific rate from parturition to peak milk yield and the descending specific rate as a measure of the lactation persistence of the milk yield at peak production. Breed and parity order of the cows were assumed as fixed effects to obtain a reliable model fitting process. Regardless of breed, first-order parity cows had greater persistency than their older counterparts, and the greater the ascending rate of milk yield from the parturition to the peak, the sharper the decrease in milk yield post-peak; therefore, the rates (absolute values) of ascending and descending phases correlated positively. Nonetheless, the actual estimated values of the descending phase rates are negative. Wood's equation was flexible enough to mimic either concave- and convex-shaped lactation profiles. The correlations between both peak milk yield and random estimates for β with total milk yield per lactation were positive. However, peak milk yield might not be the only variable used for ranking cows; the total milk yield integrates all information of the lactation profile through the estimated parameters of Wood's equation.

The elastic parameters of soft tissues are important for medical diagnosis and virtual surgery simulation. In this study, we propose a novel nonlinear parameter estimation method for soft tissues. Firstly, an in-house data acquisition platform was used to obtain external forces and their corresponding deformation values. To provide highly precise data for estimating nonlinear parameters, the measured forces were corrected using the constructed weighted combination forecasting model based on a support vector machine (WCFM_SVM). Secondly, a tetrahedral finite element parameter estimation model was established to describe the physical characteristics of soft tissues, using the substitution parameters of Young’s modulus and Poisson’s ratio to avoid solving complicated nonlinear problems. To improve the robustness of our model and avoid poor local minima, the initial parameters solved by a linear finite element model were introduced into the parameter estimation model. Finally, a self-adapting Levenberg–Marquardt (LM) algorithm was presented, which is capable of adaptively adjusting iterative parameters to solve the established parameter estimation model. The maximum absolute error of our WCFM_SVM model was less than 0.03 Newton, resulting in more accurate forces in comparison with other correction models tested. The maximum absolute error between the calculated and measured nodal displacements was less than 1.5 mm, demonstrating that our nonlinear parameters are precise.

There currently exists a mature literature on modeling the aggregate load of a distribution feeder by making use of measurements at its feeder-head at substation. The primary application of such feeder-aggregated load models is in sub-transmission or transmission system analysis. However, there is a growing need in practice also to model each individual load across the feeder. If available, such individual load models have applications in power distribution system analysis, e.g., to better integrate distributed energy resources or to improve power quality and reliability. Motivated by this observation, in this paper, we propose a new method for individual load modeling in power distribution systems. It works by using the measurements only at the feeder-head. It takes an innovative approach to analyzing the load switching events across the distribution feeder itself, instead of or in addition to relying on upstream voltage events that are commonly used in feeder-aggregated load modeling. By tracking the downstream load switching events, the proposed method can make a robust estimation of the ZIP load model parameters for all individual loads. The proposed method is examined on small illustrative test-feeders as well as the IEEE 33-bus test system under various operating scenarios. The adverse impact of errors in measurements and system parameters are also investigated on the performance of the developed load modeling method. [ABSTRACT FROM AUTHOR]

Since simulation studies are widely used in pharmacokinetics, it is necessary to ascertain their validity. An important, well-documented, concern that may negatively impact the validity of estimated parameters in pharmacokinetic models is existence of multiple minima of the criterion used in estimation. A presence of multiple minima causes instability of the estimates, dependence of the parameter estimates on the initial values, and bimodal (or, generally, multimodal) distributions of the estimated parameters. This paper offers a method to identify when these issues may occur by applying two new measures of nonlinearity. [ABSTRACT FROM AUTHOR]

This work presents a novel technique for nonlinear system identification that operates in the frequency domain and fits a model to measured spectra to estimate the parameters in a modal domain nonlinear equation of motion (EOM). Nonlinear terms are added to the linear EOM in the form of polynomials, and the proposed algorithm estimates the polynomial coefficients as well as the underlying linear Frequency Response Function (FRF). This method is an extension to a popular nonlinear system identification algorithm called NIFO, from Nonlinear Identification through Feedback of the Outputs. However, NIFO identifies the nonlinear parameters as complex numbers that may be different at each frequency line, even though the mechanical system is expected to be governed by an EOM in which the nonlinear parameters are real and constant with frequency. This might be problematic, because any variation in the identified nonlinear parameters will distort the linear FRFs estimated by NIFO, and those linear FRFs are important to tell the user whether all of the significant nonlinearity has been extracted from the system. The proposed algorithm, here dubbed Nonlinear Identification through eXtended Outputs (NIXO), estimates the nonlinear parameters as frequency-independent and real. Additionally, it is demonstrated that for the systems studied here that the algorithm works when random and swept-sine inputs are used to excite the tested structure, while NIFO only worked well when random inputs were used. The method is first evaluated numerically using benchmark case studies, starting with the SDOF equation and then reduced models of a clamped-clamped flat beam, and the results are compared to those obtained with NIFO. Then the algorithm is applied to swept-sine measurements from a 3D-printed flat beam and the results are validated by computing the primary nonlinear normal mode of the identified model and comparing it with measurements. [ABSTRACT FROM AUTHOR]

The regeneration of coked samples of methanol-to-gasoline (MTG) catalyst applied in the commercial TIGAS ( T opsoe I mproved GA soline S ynthesis) process was studied by kinetic investigations of the coke-burn rate from temperature programmed oxidation (TPO) analysis of coked catalyst from a pilot plant reactor. The spent catalyst was unloaded, and samples covering the catalyst bed from reactor inlet to exit were analyzed by means of TPO. A rigorous kinetic model was fitted to the measured evolutions of CO 2 and CO at the different catalyst bed heights. It was found that the diversity in oxidation reactivity of the coke observed along the axial reactor coordinate could be represented by lumping the various coke species into families characterized by having approximate the same oxidation reactivity. It was found that seven coke families were needed in order to obtain a satisfactory fit of the measured evolutions of CO and CO 2 . These seven coke families can roughly be divided into two categories; low-temperature (reactive) and high-temperature (less reactive) coke. Model testing is accomplished by applying the model to reconstruct a complete catalyst regeneration performed in situ in the pilot reactor. Model application illustrates that during the first step in an industrial regeneration, there is a risk of obtaining temperature excursions much higher than those expected, due to a combination of the amount of coke combusted as well as the cokes deposition along the catalyst bed length. [ABSTRACT FROM AUTHOR]

A generalized polynomial chaos (gPC)-based methodology is developed to estimate the reactivity ratio in copolymerization, where the reactivity ratio is assumed to be stochastic unknown and determined by comparing model predictions with limited experimental data. The estimation step is formulated as a stochastic inverse problem of finding the distributional stochastic reactivity ratio parameters with a maximum likelihood function. The results show that the gPC-based reactivity ratio estimation is efficient and powerful, since it simultaneously provides the best estimates and their corresponding variances. Beyond achieving accurate estimation results, it is shown that the computational cost of the gPC-based methodology is significantly lower than Markov chain Monte Carlo simulations, thus demonstrating the potential of the gPC method for dealing with other more complicated nonlinear problems. [ABSTRACT FROM AUTHOR]

A new Wide-Area Monitoring System (WAMS) application for monitoring inter-area oscillations, based on the Newton-type algorithm is presented in this paper. The core of this novel WAMS application is a numerical algorithm for the real-time estimation of the dominant inter-area oscillation mode, which processes Global Positioning System synchronized information obtained from Phasor Measurement Units installed in the power system. The parameter model is a highly nonlinear function, making this a particularly difficult challenge. Two data sets were tested using the new algorithm, one based on simulated models and the other based on real-life data records. The computer simulated model was based on a known architecture with signals obtained using a dynamic simulation of a multi-machine power system. The real-life data records used information collected on the FlexNet Wide-Area Monitoring System (FlexNET-WAMS) installed in the Great Britain power grid. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

By itself, a wind turbine is already a fairly complex system with highly nonlinear dynamics. Changes in wind speed can affect the dynamic parameters of wind turbines, thus rendering the parameters uncertain. However, we can identify the dynamics of the wind energy conversion system (WECS) online by a quasi-ARX neural network (QARXNN) model. A QARXNN presents a problem in searching for the coefficients of the regression vector (input vector). A multilayer perceptron neural network (MLPNN) is an embedded system that provides the unknown parameters used to parameterize the input vector. Fascinatingly, the coefficients of the input vector from prediction model can be set as controller parameters directly. The stability of the closed-loop controller is guaranteed by the switching of the linear and nonlinear parts of the parameters. The dynamic of WECS is derived with given parameters, and then a wind speed signal created by a random model is fed to the system causing uncertainty parameters and reducing the power that can be absorbed from wind. By using a minimum variance controller, the maximum power is tracked from WECS. From the simulation results, it is observed that the proposed controller is effective in tracking the maximum power of WECS. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Power system loads are one of the crucial elements of modern power systems and, as such, must be properly modelled in stability studies. However, the static and dynamic characteristics of a load are commonly unknown, extremely nonlinear, and are usually time varying. Consequently, a measurement-based approach for determining the load characteristics would offer a significant advantage since it could update the parameters of load models directly from the available system measurements. For this purpose and in order to accurately determine load model parameters, a suitable parameter estimation method must be applied. The conventional approach to this problem favors the use of standard nonlinear estimators or artificial intelligence (AI)-based methods. In this paper, a new solution for determining the unknown load model parameters is proposed—an improved particle swarm optimization (IPSO) method. The proposed method is an AI-type technique similar to the commonly used genetic algorithms (GAs) and is shown to provide a promising alternative. This paper presents a performance comparison of IPSO and GA using computer simulations and measured data obtained from realistic laboratory experiments. [ABSTRACT FROM AUTHOR]

Damping is often assumed to viscous and linear in modelling a dynamic system. However, damping is inherently nonlinear and often is in non-polynomial forms such as Coulomb damping, bilinear, or quadratic damping. Recently, non-polynomial forms of damping have found wide applications in vibration isolators and absorbers. However, not much study is available for the identification of these non-polynomial forms of damping. The present work attempts to develop an identification and parameter estimation procedure for two non-polynomial damping forms, i.e., bilinear damping and quadratic damping. First, response harmonic amplitudes are formulating using Volterra series and equivalent linearised damping; and then they are compared with representative polynomial forms, i.e., square damping and cubic damping. Bilinear damping was identified employing a method of harmonic probing and studying first and second harmonic amplitude characteristics. Similar way, quadratic damping is identifying using first and third harmonic characteristics. Finally, a parameter estimation procedure is developed as a step-by-step algorithm and demonstrated through numerical simulation and error analysis. It is shown that reasonable accuracy can be obtained in estimation of the nonlinear parameters with proper selection of excitation frequencies and excitation levels. • Identification and classification of non-polynomial forms of damping nonlinearity has been discussed. • Response characteristics of non-polynomial and polynomial forms of damping nonlinearities are compared. • Higher order FRF synthesis formula developed using Volterra series for bilinear and quadratic damping nonlinearities. • A parameter estimation algorithm is developed for bilinear and quadratic damping nonlinearities. • Numerical simulation and error analysis demonstrate accuracy in estimating nonlinear and linear parameters. [ABSTRACT FROM AUTHOR]

This paper presents the results of the development of Smart Grid transmission network applications in the Great Britain (GB) power system. A new Wide Area Monitoring System (WAMS) application for monitoring inter-area oscillations is developed. The core of this novel application is a fast nonlinear algorithm for the real-time estimation of the dominant inter-area oscillation mode, which processes GPS synchronized information obtained from Phasor Measurement Units (PMUs) installed in the power system. It is based on the Newton-Type Algorithm (NTA), an efficient parameter estimator. The paper focuses on the practical application of the new WAMS application: two data sets were tested, one based on computer simulations and the other based on real-life data records. The computer simulated oscillatory signals were obtained through dynamic simulations of the full GB power system model consisting of over 200 generators. The real-life data records used information collected by the FlexNet Wide Area Monitoring System (FlexNET-WAMS) installed in the GB network. Based on these data records, the features of inter-area oscillations in the GB network are drawn. [ABSTRACT FROM PUBLISHER]

We present an inversion of the Hodgkin-Huxley formalism to estimate initial conditions and model parameters, including functions of voltage, from the solutions of the underlying ordinary differential equation (ODE) subjected to multiple voltage step stimulations. As such, the procedure constitutes a means to estimate the parameters including functions of voltage of an Hodgkin-Huxley formalism from experimental data. The basic idea was developed in a previous communication (SIAM J. Appl. Math. 64:1264-1274, ). The inversion in question applies to currents exhibiting activation and inactivation, but the version, as published previously, cannot estimate the unknowns for channels that rapidly inactivate just after a brief opening. In such cases, the amplitude of the current, in a given voltage range, is too small to be detectable by the instrumentation using previously applied experimental protocols. This is, for example, the case for the sodium channels in a number of excitable tissue for potential in the vicinity of the cell resting potential. The current communication extends the inversion procedure in a manner to overcome this limitation. Furthermore, within the inversion framework, we can determine whether the data at the basis of the estimation sufficiently constrains the estimation problem, i.e., whether it is complete. We exploit this element of our method to document a set of stimulation protocols that constitute a complete data set for the purpose of inverting the Hodgkin-Huxley formalism. [ABSTRACT FROM AUTHOR]

Abstract: A modified Brookfield viscometer equipped with a data acquisition system was used to study gelatinizing behavior of native corn starch. Data for the dependent variable (continuous torque), and independent variables were collected. The independent variables were time–temperature history, shear history, and temperature, corresponding to the three main regions in a pasting curve. Parameters were estimated simultaneously using both ordinary least squares, and the sequential method. The model fit well as shown by RMSE of approximately 2% of full scale, and relative standard error of all parameters estimated less than 11%. Scaled sensitivity coefficients showed that the most important parameters were time–temperature history, followed by shear history, and the temperature parameter was the least important. This work is the first to show that starch-pasting curve parameters can be estimated simultaneously and sequentially, and can be used to predict pasting curves in other systems (RVA) well. [Copyright &y& Elsevier]

Neuronal activity can evoke the hemodynamic change that gives rise to the observed functional magnetic resonance imaging (fMRI) signal. These increases are also regulated by the resting blood volume fraction ( V) associated with regional vasculature. The activation locus detected by means of the change in the blood-oxygen-level-dependent (BOLD) signal intensity thereby may deviate from the actual active site due to varied vascular density in the cortex. Furthermore, conventional detection techniques evaluate the statistical significance of the hemodynamic observations. In this sense, the significance level relies not only upon the intensity of the BOLD signal change, but also upon the spatially inhomogeneous fMRI noise distribution that complicates the expression of the results. In this paper, we propose a quantitative strategy for the calibration of activation states to address these challenging problems. The quantitative assessment is based on the estimated neuronal efficacy parameter $$\epsilon$$ of the hemodynamic model in a voxel-by-voxel way. It is partly immune to the inhomogeneous fMRI noise by virtue of the strength of the optimization strategy. Moreover, it is easy to incorporate regional vascular information into the activation detection procedure. By combining MR angiography images, this approach can remove large vessel contamination in fMRI signals, and provide more accurate functional localization than classical statistical techniques for clinical applications. It is also helpful to investigate the nonlinear nature of the coupling between synaptic activity and the evoked BOLD response. The proposed method might be considered as a potentially useful complement to existing statistical approaches. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we propose that the dynamic evolution of EEG activity during epileptic seizures may be characterised as a path through parameter space of a neural mass model, reflecting gradual changes in underlying physiological mechanisms. Previous theoretical studies have shown how boundaries in parameter space of the model (so-called bifurcations) correspond to transitions in EEG waveforms between apparently normal, spike and wave and subsequently poly-spike and wave activity. In the present manuscript, we develop a multi-objective genetic algorithm that can estimate parameters of an underlying model from clinical data recordings. A standard approach to this problem is to transform both clinical data and model output into the frequency domain and then choose parameters that minimise the difference in their respective power spectra. Instead in the present manuscript, we estimate parameters in the time domain, their choice being determined according to the best fit obtained between the model output and specific features of the observed EEG waveform. This results in an approximate path through the bifurcation plane of the model obtained from clinical data. We present comparisons of such paths through parameter space from separate seizures from an individual subject, as well as between different subjects. Differences in the path reflect subtleties of variation in the dynamics of EEG, which at present appear indistinguishable using standard clinical techniques. [Copyright &y& Elsevier]

The microwave limb sounder is one of four instruments on the NASA Earth Observing System Aura spacecraft, launched 15 July 2004 into a near-polar sun-synchronous ~705 km altitude orbit, which gives global coverage, day and night, with ~13 orbits per day. The instrument passively views thermal emission from the limb of the Earth's atmosphere in ~1,000 microwave channels, viewing forward in the satellite flight direction. The antenna scans in the orbit plane from the surface to ~100 km altitude at the limb every ~25 s, a total of ~3,500 scans per day with 148 integration periods per scan, giving >500 million measurements per day. We solve for ~20 quantities that characterize the upper atmosphere at each of ~70 levels on each scan, or ~5 million results per day. [ABSTRACT FROM AUTHOR]

Abstract: The physics-based single particle (SP) model was compared to the semi-empirical equivalent circuit analog (ECA) model to predict the cell voltage under constant current charge and discharge for different sets of Li-ion cell data. The parameters of the models were estimated for each set of data using nonlinear least squares regression. In order to enhance the probability of finding the global optima, a combination of the trust region method with a genetic algorithm was applied to minimize the objective function (the sum of squared residuals). Several statistical quantities such as sum of the squared errors, adjusted R 2, root mean squared error, confidence intervals of the parameters, and prediction bounds were included to compare the models. A significance test (t test) on the parameters and the analysis of the variances (F and χ 2 tests) were also performed to discriminate between the goodness of the fit obtained from the two models. The statistical results indicate that the SP model superiorly predicts all sets of data compared to the ECA model, while the computation times of both models are on the same order of magnitude. [Copyright &y& Elsevier]

Abstract: Design of an optimal input signal in system identification using a multi-layer perceptron network is treated. Neural networks of the same structure differing only in parameter values are able to approximate various nonlinear mappings. To ensure high quality of network parameter estimates, it is crucial to find a suitable input signal. It is shown that utilizing the conditional probability density function of parameters for design of the input signal provides better results than currently used procedures based on parameter point estimates only. The conditional probability density function of parameters is unknown and hence it is estimated using the Gaussian sum approach approximating arbitrary probability density function by a sum of normal distributions. This approach is less computationally demanding than the Markov Chain Monte Carlo method and achieves better results in comparison with the commonly used local prediction error methods. The properties of the proposed input signal designs are illustrated in numerical examples. [ABSTRACT FROM AUTHOR]

Abstract: In this contribution we address the issue of estimating parameters of a process, which is described by a set of first order, quasi-linear partial differential equations (PDEs) from a set of measurements. The parameters are found by minimizing the sum of the square of the errors over a finite set of measurement data. The errors are defined as the differences between the model outputs and corresponding measurements. Since the assumption of constant model parameters may not be realistic in many practical applications, the estimation is carried out using a moving and fixed-size window of data. When a new measurement becomes available, the oldest measurement is discarded and the new one is added. [Copyright &y& Elsevier]

The models of gene regulatory networks are often derived from statistical thermodynamics principle or Michaelis-Menten kinetics equation. As a result, the models contain rational reaction rates which are nonlinear in both parameters and states. It is challenging to estimate parameters nonlinear in a model although there have been many traditional nonlinear parameter estimation methods such as Gauss-Newton iteration method and its variants. In this article, we develop a two-step method to estimate the parameters in rational reaction rates of gene regulatory networks via weighted linear least squares. This method takes the special structure of rational reaction rates into consideration. That is, in the rational reaction rates, the numerator and the denominator are linear in parameters. By designing a special weight matrix for the linear least squares, parameters in the numerator and the denominator can be estimated by solving two linear least squares problems. The main advantage of the developed method is that it can produce the analytical solutions to the estimation of parameters in rational reaction rates which originally is nonlinear parameter estimation problem. The developed method is applied to a couple of gene regulatory networks. The simulation results show the superior performance over Gauss-Newton method. [ABSTRACT FROM AUTHOR]

The stepwise regression is a well-established technique used to determine aerodynamic model structure from flight data. This paper discusses the application of stepwise regression to a nonlinear estimation method proposed previously by the authors, for model structure determination. The approach can also be used for updating aerodynamic database models in table look-up form. The current results obtained for model structure determination for a reference problem are compared against the results obtained using the well known least squares multivariate linear estimation. Simulated data of a transport aircraft is used to prove the concept. Finally, an application of the technique for the aero database update of a high performance fighter aircraft is discussed. [ABSTRACT FROM AUTHOR]

A single-blow transient testing technique considering the effect of longitudinal heat conduction is suggested for determining the average convection heat transfer coefficient of compact heat exchanger surface. By matching the measured outlet fluid temperature variation with similar theoretical curves, the dimensionless longitudinal conduction parameter λ1, the time constant of the inlet fluid temperature τ+, and the number of heat transfer units N tu can be determined simultaneously using the Levenberg-Marquardt nonlinear parameter estimation method. Both sensitivity analysis and numerical experiments with simulated measurements containing random errors show that the method in the present investigation provides satisfactory accuracy of the estimated parameter N tu, which characterizes the heat transfer performance of compact heat exchanger surfaces. [ABSTRACT FROM AUTHOR]

Computationally scalable and accurate estimation, prediction, and simulation of wireless communication channels is critical to the development of more adaptive transceiver algorithms. Previously, the application of autoregressive moving average (ARMA) modeling to fading processes has been complicated by ill-conditioning and nonlinear parameter estimation. This correspondence presents a numerically stable and accurate method to synthesize ARMA rational approximations of correlated Rayleigh fading processes from more complex higher order representations. Here, the problem is decomposed into autoregressive (AR) model matching followed by linear system identification. Performance is compared to that of AR, inverse discrete Fourier transform, and sum of sinusoids techniques. Also, for the first time, the finite-precision performance of different methods is compared. [ABSTRACT FROM PUBLISHER]