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• An accurate gradient analysis based on an adaptive recursive SBFEM. • A reliable interval estimation based on Taylor series expansion and adaptive recursive computing. • A two-stage identification strategy to reduce the scale and complexity of inverse analysis. • Convenient treatment of stress singularity and unbounded domain in interval analysis with sufficient accuracy. • An alternative way to evaluate the uncertainty of constitutive parameters with interval uncertain measurement. This paper aims to develop efficient algorithms for both forward and inverse interval viscoelastic analysis. Utilizing an adaptive recursive scaled boundary finite element method (SBFEM), the solution of the forward deterministic solution is approximated using Taylor series expansion, based upon which the interval bounds of displacement/stress can be estimated by interval arithmetic when constitutive parameters are interval variables. On the other hand, the uncertainties of constitutive parameters caused by the uncertain measurements are estimated in terms of their central values and radii, which are identified on the framework of a gradient-based two-stage strategy. At each stage, the process of identification is treated as two deterministic optimization problems that are solved by the Levenberg-Marquardt method. By combining SBFEM with a temporally piecewise adaptive algorithm (TPAA), the proposed method excels in solving singularity and unbounded domain problems with interval uncertainty and in computing intervals of displacements/stresses with stable temporal solution accuracy with different step sizes. Three numerical examples are provided to verify the effectiveness of the proposed approaches, and all the presented results agree well with the references. For the forward interval problem, the bound estimations of stresses near a crack tip and displacements in an unbounded domain are emphasized; for the inverse interval problems, the impacts of initial guesses and locations of measurement points, and regional inhomogeneity are taken into account. [ABSTRACT FROM AUTHOR]

In this paper, the containment control problem of heterogeneous uncertain high-order linear Multi-Agent Systems (MASs) is addressed and solved via a novel fully-Distributed Model Reference Adaptive Control (DMRAC) approach, where each follower computes its adaptive control action on the basis of local measurements, information shared with neighbors (within the communication range) and the matching errors w.r.t. its own reference model, without requiring any previous knowledge of the global directed communication topology structure. The approach inherits the robustness of the direct model reference adaptive control (MRAC) scheme and allows all agents converging towards the convex hull spanned by leaders while fulfilling at the same time local additional performance requirements at single-agent level, such as prescribed settling time, overshoot, etc. The asymptotic stability of the whole closed-loop network is analytically derived by exploiting the Lyapunov theory and the Barbalat lemma, hence proving that each follower converges to the convex hull spanned by the leaders, as well as the boundedness of the adaptive gains. Extensive numerical analysis for heterogeneous MAS composed of stable, unstable and oscillating agent dynamics are presented to validate the theoretical framework and to confirm the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, an efficient adaptive cross approximation (ACA) method is employed for the skin effect and proximity effect analysis. Multi-layer stranded cables with multiple strands and different materials are used for analysis to approximate the effect. The skin and proximity matrices are computed by the ACA low-rank matrix approximation technique. The ACA method can provide better solutions through different iterations. The ACA algorithm is very stable and useful for calculating a low rank matrix approximation, because the adaptive mesh refinement for generating optimal computation. The ACA approach provides the less computational memory and less CPU time. Compare the calculated results with standard work results (such as reference) to verify the effectiveness of the adaptive cross-approximation algorithm. [ABSTRACT FROM AUTHOR]

In service computing, combining multiple services through service composition to address complex user requirements has become a popular research topic. QoS-aware service composition aims to find the optimal composition scheme with the QoS attributes that best match user requirements. However, certain QoS attributes may continuously change in a dynamic service environment, so service composition methods need to be adaptive. Furthermore, the large number of candidate services poses a key challenge for service composition, where existing service composition approaches based on reinforcement learning (RL) suffer from low efficiency. To deal with the problems above, in this paper, a new service composition approach is proposed which combines RL with skyline computing where the latter is used for reducing the search space and computational complexity. A WSC-MDP model is proposed to solve the large-scale service composition within a dynamically changing environment. To verify the proposed method, a series of comparative experiments are conducted, and the experimental results demonstrate the effectiveness, scalability and adaptability of the proposed approach. [ABSTRACT FROM AUTHOR]

Abstract In real-world applications, the performances of traditional fuzzy interpolation methods are not good enough because they may produce inconsistent fuzzy interpolation results when contradictory fuzzy interpolated results are obtained after fuzzy interpolation processes. In recent years, several adaptive fuzzy rule interpolation (AFRI) methods have been proposed for identification and correction of the contradictory fuzzy interpolated results. In this paper, we propose a new adaptive weighted fuzzy rule interpolation (AWFRI) method based on ranking values and similarity measures of rough-fuzzy sets (RFSs). If the degree of similarity obtained between the fuzzy interpolation results is less than a predefined threshold value, the proposed AWFRI method applies the proposed transformation and modification techniques to solve the contradiction between fuzzy interpolation results. The experimental results show that the proposed AWFRI method can overcome the drawbacks of the existing AFRI methods because it obtains higher consistency degrees between fuzzy interpolation results and it deals with AWFRI based on RFSs rather than type-1 fuzzy sets or interval type-2 fuzzy sets. [ABSTRACT FROM AUTHOR]

Abstract The existence of imbalanced classes can considerably degrade the performance of most standard learning algorithms. This paper presents a new imbalanced learning method called AWGSENN to address this problem on data level. In AWGSENN, each minority instance is firstly weighted based on the number of majority class neighbors and the distance of the minority instance to its neighbors. Then, a Gaussian distribution probability density function is designed to generate new instances nonlinearly. Finally, the edited nearest neighbor rule is used as a data cleaning technique to remove overlapping and noisy instances. The proposed method is evaluated in extensive experiments by comparing it with five over-sampling and two hybrid sampling methods on 37 data sets from the KEEL data repository. Empirical study results show that our approach can achieve significant performance improvement on G-mean and the area under curve metrics. Wilcoxon signed-rank test results show that our approach is superior to other resampling approaches. We apply the proposed method for Android malware detection, and the results further demonstrate the promising performance of our approach. [ABSTRACT FROM AUTHOR]

Highlights • An adaptive version of the Particle Swarm Optimization algorithm is proposed. • All the parameters of the algorithm are calculated during the optimization process. • Comparison with other PSO implementations and other algorithms from the literature. • The algorithm is ranked in the 3 most effective algorithms applied for the VRPTW. • The algorithm is tested in 356 instances with nodes varying between 100 and 1000. Abstract In this paper, a new variant of the Particle Swarm Optimization (PSO) algorithm is proposed for the solution of the Vehicle Routing Problem with Time Windows (VRPTW). Three different adaptive strategies are used in the proposed Multi-Adaptive Particle Swarm Optimization (MAPSO) algorithm. The first adaptive strategy concerns the use of a Greedy Randomized Adaptive Search Procedure (GRASP) that is applied when the initial solutions are produced and when a new solution is created during the iterations of the algorithm. The second adaptive strategy concerns the adaptiveness in the movement of the particles from one solution to another where a new adaptive strategy, the Adaptive Combinatorial Neighborhood Topology, is used. Finally, there is an adaptiveness in all parameters of the Particle Swarm Optimization algorithm. The algorithm starts with random values of the parameters and based on some conditions all parameters are adapted during the iterations. The algorithm was tested in the two classic sets of benchmark instances, the one that includes 56 instances with 100 nodes and the other that includes 300 instances with number of nodes varying between 200 and 1000. The algorithm was compared with other versions of PSO and with the best performing algorithms from the literature. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a novel differential evolution algorithm for numerical optimization by designing the neighborhood-based mutation strategy and adaptive evolution mechanism. In the proposed strategy, two novel neighborhood-based mutation operators and an individual-based selection probability are developed to adjust the search performance of each individual suitably. Meanwhile, the evolutionary dilemmas of the neighborhood are identified by tracking its performance and diversity, and alleviated by designing a dynamic neighborhood model and two exchanging operations in the proposed mechanism. Furthermore, the population size is adaptively adjusted by a simple reduction method. Differing from differential evolution variants based on neighborhood and evolutionary state, the proposed algorithm makes full use of the characteristics of individuals, identifies and alleviates the neighborhood evolutionary dilemmas of each individual. Compared with 21 typical algorithms, the numerical results on 30 benchmark functions from CEC2014 show that the proposed algorithm is reliable and has better performance. [ABSTRACT FROM AUTHOR]

Due to errors in measurements and inherent variability in the quantities of interest, models based on random differential equations give more realistic results than their deterministic counterpart. The generalized polynomial chaos (gPC) is a powerful technique used to approximate the solution of these equations when the random inputs follow standard probability distributions. But in many cases these random inputs do not have a standard probability distribution. In this paper, we present a step-by-step constructive methodology to implement directly a useful version of adaptive gPC for arbitrary distributions, extending the applicability of the gPC. The paper mainly focuses on the computational aspects, on the implementation of the method and on the creation of a useful software tool. This tool allows the user to easily change the types of distributions and the order of the expansions, and to study their effects on the convergence and on the results. Several examples illustrating the usefulness of the method are included. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of direct adaptive neural network (NN) tracking control for a class of uncertain nonlinear multi-input/multi-output (MIMO) systems by employing backstepping technique. Compared with the existing results, the outstanding features of the two proposed control schemes are presented as follows. Firstly, a semi-globally stable adaptive neural control scheme is developed to guarantee that the ultimate tracking errors satisfy the accuracy given a priori , which cannot be carried out by using all existing adaptive NN control schemes. Secondly, we propose a novel adaptive neural control approach such that the closed-loop system is globally stable, and in the meantime the ultimate tracking errors also achieve the tracking accuracy known a priori , which is different from all existing adaptive NN backstepping control methods where the closed-loop systems can just be ensured to be semi-globally stable and the ultimate tracking accuracy cannot be determined a priori by the designers before the controllers are implemented. Thirdly, the main technical novelty is to construct three new n th-order continuously differentiable switching functions such that multiswitching-based adaptive neural backstepping controllers are designed successfully. Fourthly, in contrast to the classic adaptive NN control schemes, this paper adopts Barbalat׳s lemma to analyze the convergence of tracking errors rather than Lyapunov stability theory. Consequently, the accuracy of ultimate tracking errors can be determined and adjusted accurately a priori according to the real-world requirements, and all signals in the closed-loop systems are also ensured to be uniformly ultimately bounded. Finally, a simulation example is provided to illustrate the effectiveness and merits of the two proposed adaptive NN control schemes. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, (1) a new fuzzy model is presented to simulate two different chaotic systems with different numbers of nonlinear terms and (2) a new adaptive approach and a new control Lyapunov function are proposed to synchronize these two different fuzzy chaotic systems and speed up the convergence of errors. By using this new model, the numbers of fuzzy rules of chaotic systems can be reduced from to and only 2 subsystems are needed, where N is the number of nonlinear terms. The fuzzy systems become much simpler. In addition, through the new fuzzy model, the new fuzzy systems are much simpler than T–S fuzzy systems (when nonlinear systems are complicated) and can be used to any other kind of application in fuzzy logic control or fuzzy modeling. Mathieu–Van der Pol system (which is called M–V system in this paper) and Quantum cellular neural networks nanosystem (which is called Q-CNN system in this paper) are used for illustrations in numerical simulation results to show the effectiveness and feasibility of our new adaptive approach and new control Lyapunov function. The T–S fuzzy modeling and traditional adaptive control are also given in Appendices B and C for comparison. [Copyright &y& Elsevier]

Being able to implement efficient cross-organizational collaborations has become a key factor for enterprises to respond to emerging market opportunities. The business process management approach is commonly used to design cross-organizational collaborations. This type of business process aims at achieving specific collaborative objectives by addressing three main steps according to a top-down approach: (i) defining the business services that have to be performed to reach the objectives, (ii) finding the best set of partners to provide them and (iii) ordering the business services in an optimized way. While the resulting business processes are a cornerstone to support the interoperability among the partners of a collaboration, their design step remains often humanly-conducted and laborious. Moreover, seeking the “best” set of partners involves non-additive criteria such as the delivery time (i.e. business services can be performed in sequence or in parallel within the process). In this context, this paper presents a decision support system based on an Ant Colony Optimization algorithm to exploit collaborative knowledge gathered from companies on a dedicated platform (companies’ profile models registered to the platform and collaborative opportunity models) and deduce quasi-optimal collaborative business processes. A prototype that supports this system is also presented. [ABSTRACT FROM AUTHOR]

Recently, event-based social networks (EBSNs) have become popular, hence how to organize a social event has received significant attention. Most of prior studies about social events organization usually consider the willingness of attendees and their relationships. However, they ignore the roles of attendees. In fact, many social events have requirement of attendees roles in the real world. In this paper, we propose to study the problem of Multi-Role Social Event Organization (MRSEO). Our goal is to maximize the overall harmony of the social event while considering multiple factors, such as attendees’ roles, willingness and their relationships. To solve the problem, we propose two algorithms. Firstly, we propose a continuous relaxation technique based algorithm, called MRSEO-CRA. It converts the problem of MRSEO to an equivalent unconstrained continuous problem, and then employs RatioDCA algorithm to solve the converted one. Secondly, to better trade off between performance and running time, we further propose the other algorithm based on improved PageRank, called MRSEO-IPR. We conduct extensive experiments on real-world datasets to evaluate these two proposed algorithms and experimental results show that our algorithms outperform the state-of-the-art algorithm in terms of performance and running time. [ABSTRACT FROM AUTHOR]

In this paper, the problem of detrending a time series and/or estimating a wandering baseline is addressed. We propose a new methodology that adaptively minimizes different regularized cost functions by introducing an ARMA model of the underlying trend. Mixed ℓ 1 /ℓ 2 -norm penalty functions are taken into consideration and novel RLS and LMS solutions are derived for the model parameters estimation. The proposed methods are applied to typical trend estimation/removal problems that can be found in the analysis of economic time series or biomedical signal acquisition. Comparisons with standard noncausal filtering techniques are also presented. [ABSTRACT FROM AUTHOR]

This paper investigates the trajectory tracking problem of quadrotor systems in the presence of uncertain inertial parameters. The controller is based on the hierarchical control strategy. The main features of this research include: (i) the command control of the position loop is synthesized with a saturation scheme, and a novel immersion & invariance (I&I) adaptation methodology is designed such that the mass estimation converges asymptotically to its real value; (ii) as an intermediate step, the command rotation matrix is extracted from the command control without the singularity; (iii) in terms of the rotation matrix property, the applied torque with appropriate control parameters and initial conditions is exploited for the attitude loop; and (iv) the projection algorithm is used to guarantee the inertial matrix estimation evolving within a prescribed range. It is proven based on the hierarchical system theory that, the developed controller enables the quadrotor system to track the reference trajectory asymptotically. Simulations are provided to support the proposed theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, under the circumstance of parametric uncertainties and external load disturbance, an adaptive fuzzy output feedback and command filtering error compensation control is proposed for permanent magnet synchronous motors (PMSMs) in electric vehicle systems. First, fuzzy logic systems (FLSs) are used to approximate unknown nonlinear functions, and a fuzzy reduced-order observer is set up to estimate the angle speed of the PMSMs. Then, the command filtering error compensation control is utilized to handle the “explosion of complexity” problem arose from the derivation of virtual control functions and reduce the error caused by the command filter, which improves the control accuracy. In addition, the adaptive backstepping technique is exploited to construct the adaptive fuzzy controllers to guarantee the tracking error converge to a small neighborhood of the origin. Finally, simulation results verify the effectiveness and advantages of the proposed theoretic result. [ABSTRACT FROM AUTHOR]

In this paper, we propose sparse adaptive channel estimation algorithms based on a mixed controlled l 2 and l p -norm error criterion and zero attracting theory. In the proposed algorithms, a controlling parameter within the range of [0, 1] is adopted to control the mixture of the l 2 and l p norms which are exerted on the estimation error. The sparsity-aware characteristic is implemented by an l 1 -norm penalty, a correntropy-induced metric penalty and a log-sum function constraint which are to exploit the in-nature sparseness of the channels. The proposed sparsity-aware algorithms give desired zero attractors in their iterations to speed up the convergence. The derivation of the proposed algorithms is presented in detail. We can find that the previously proposed sparsity-aware algorithms can be regarded as a special case of the proposed sparse adaptive algorithms. Also, the behaviors of the proposed algorithms are well verified over a multi-path wireless communication channel. As a result, our proposed algorithms are superior to the previously reported sparse mixed adaptive filters with respect to both the convergence and steady-state error for handling sparse signals. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel self-embedding fragile image watermarking scheme for tampering recovery based on reference-data interleaving mechanism and adaptive selection of embedding mode. During watermark embedding, reference bits are derived from the interleaved, scrambled MSB bits of original image, and then are combined with authentication bits to form the watermark bits for LSB embedding. Different with the reported schemes with the fixed embedding mode, the proposed scheme not only has two types of embedding modes, i.e., overlapping-free embedding and overlapping embedding, but also utilizes the adaptively flexible numbers of MSB and LSB layers to achieve satisfactory performances for different tampering rates. Also, detailed analyses are given to provide the theoretical values of watermarked-image quality, perfect recovery probability, and recovered-image quality, which are used to conclude the optimal choice of embedding modes. Experimental results show the effectiveness and superiority of the proposed scheme compared with some state-of-the-arts schemes. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive denoising methodology for electrocardiogram (ECG) signals that employs ensemble empirical mode decomposition (EEMD) and a genetic algorithm (GA)-based thresholding technique. In this method, a noisy ECG signal is first decomposed by means of EEMD into a series of intrinsic mode functions (IMFs), which are then separated into signal- and noise-dominant groups using a similarity measure based on Kullback-Leibler divergence and a probability density function. A GA-based thresholding technique is then used adaptively to remove the noise inherent in noise-dominant IMFs. Finally, a denoised signal is reconstructed by combining the signal-dominant IMFs and the denoised noise-dominant IMFs. The performance of the proposed denoising methodology is evaluated in the present work by using the MIT-BIH arrhythmia ECG database, and results from the proposed method are compared with those of other conventional approaches in various noisy environments. Experimental results indicate that the proposed denoising method outperforms other denoising methodologies in terms of signal-to-noise ratio, mean square error, and percent root mean square difference. [ABSTRACT FROM AUTHOR]

Finite-time consensus for multi-agent systems (MAS) with high-order nonlinear uncertain dynamics under directed communication topology is still an open problem due to the involvement of high-order nonlinear uncertain dynamics; one-way directed communication constraints; and unknown time-varying control effectiveness gain. In this paper, based upon the locally defined consensus error and fractionally composed virtual error, a number of useful intermediate results are derived, with which, a finite time consensus solution is established for networked MAS with high-order uncertain dynamics under single-way directed communication topology. By including fraction power integration as part of the Lyapunov function candidate and by using inductive analysis, it is shown that the proposed distributed solution is able to achieve consensus in finite time with sufficient accuracy. The benefits and effectiveness of the developed algorithm are also confirmed by numerical simulations. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed consensus problem of second-order multi-agent systems with nonlinear dynamics for both the cases leaderless and with a leader. First, based only on local information of the network structure, a new type of distributed adaptive consensus protocol is proposed, namely, the edge-based adaptive protocol which assigns a time-varying coupling weight to each edge in the communication. The coupling gains can be chosen randomly in the protocol and the consensus of second-order multi-agent systems can be reached without satisfying any additional conditions. Moreover, the leader–follower consensus problem in multi-agent systems with updated control gains is studied. To remove the limitation of coupling gains, a fully distributed adaptive protocol is designed. Under the proposed protocol, the leader–follower consensus can be also achieved without satisfying any additional conditions. Finally, two examples are presented to illustrate the theoretical analysis. [ABSTRACT FROM AUTHOR]

Model reference adaptive controllers designed via the Minimal Control Synthesis (MCS) approach are a viable solution to control plants affected by parameter uncertainty, unmodelled dynamics, and disturbances. Despite its effectiveness to impose the required reference dynamics, an apparent drift of the adaptive gains, which can eventually lead to closed-loop instability or alter tracking performance, may occasionally be induced by external disturbances. This problem has been recently addressed for this class of adaptive algorithms in the discrete-time case and for square-integrable perturbations by using a parameter projection strategy [1] . In this paper we tackle systematically this issue for MCS continuous-time adaptive systems with integral action by enhancing the adaptive mechanism not only with a parameter projection method, but also embedding a σ -modification strategy. The former is used to preserve convergence to zero of the tracking error when the disturbance is bounded and L 2 , while the latter guarantees global uniform ultimate boundedness under continuous L ∞ disturbances. In both cases, the proposed control schemes ensure boundedness of all the closed-loop signals. The strategies are numerically validated by considering systems subject to different kinds of disturbances. In addition, an electrical power circuit is used to show the applicability of the algorithms to engineering problems requiring a precise tracking of a reference profile over a long time range despite disturbances, unmodelled dynamics, and parameter uncertainty. [ABSTRACT FROM AUTHOR]

This paper addresses adaptive synchronization problem for complex networks with probabilistic time-varying delays. Using delay partitioning method and integral inequalities, combining with reciprocal convex combination approach technique, some delay-dependent synchronization stability criteria are derived. Finally, two numerical examples including Watts–Strogatz random complex networks and regular networks are provided to illustrate the importance of our theoretical analysis. [ABSTRACT FROM AUTHOR]

In this paper, we propose an efficient adaptive scheme based on the WENO (weighted essentially non-oscillatory) process for the hyperbolic conservation laws. This scheme achieves fifth order accuracy as the fifth order WENO scheme does. Also, due to its adaptive mechanism, the scheme deals with problems which contain both discontinuities and complex smooth regions much better than traditional high order schemes. In addition, the proposed scheme saves computational costs by avoiding solving redundant nonlinear weights in certain places. Systematic analyses and numerical tests show that our adaptive scheme is of a high level of robustness and resolution. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive distributed consensus tracking control approach for uncertain nonlinear multi-agent systems in pure-feedback form under a directed topology where each follower is dominated by dynamic uncertainties and unmeasured states. Radial basis function neural networks (RBFNNs) are employed to compensate the unknown nonlinear functions obtained by recursive design procedure for followers. The distributed dynamic surface controllers are able to eliminate the condition in which the approximation error of the traditional neural networks is bounded. By introducing an available dynamic signal and two smooth scalar functions, the obstacle caused by unmodeled dynamics is conquered. The main advantage of the proposed method is that for M pure-feedback nonlinear followers, only one learning parameter needs to be updated online. It is also shown that the proposed consensus controller can guarantee cooperatively semi-global uniform ultimate boundedness (CSUUB) of all the signals, and the consensus errors converge to an adjustable neighborhood of the origin. [ABSTRACT FROM AUTHOR]

Differential evolution is an excellent optimizer for single objective optimization problems. To extend its use for multi-objective optimization problems with promising performance, this paper proposes a grid-based adaptive multi-objective differential evolution algorithm. The main feature of the proposed algorithm is its dynamical adjustment of convergence and diversity by exploiting the feedback information during the evolutionary process. In the algorithm, the objective space is divided into grids according to the nondominated solutions in the population. Based on the grid, three indexes including grid fitness, grid density, and grid-objective-wise standard deviation are defined to measure individual rank, individual density, and population search status quo, respectively. Afterwards, three main components of the algorithm, i.e., parents selection, parameter control, and population update, are implemented based on grid index values. To validate algorithm performance, comprehensive experiments are carried out on thirty-one benchmark problems. The results show that the proposed algorithm outperforms nine state-of-the-art competitors in terms of three performance metrics. Also, the effectiveness of three components and the sensitivity of two design parameters in the algorithm are empirically quantified. [ABSTRACT FROM AUTHOR]

Congestion in wireless sensor networks (WSNs) can result in the phenomenon of packet loss, which in turn reduces throughput and wastes energy. Therefore, congestion in WSNs needs to be controlled to achieve the goals of high energy efficiency, prolonged system lifetime, and better fairness and quality of service. We propose an adaptive multi-path approach based on an improved leapfrog algorithm to solve the transmission-congestion problem in WSNs. Specifically, this paper establishes a path-satisfaction model that considers the predicted degree of congestion, the remaining energy, and the minimum number of hops. The algorithm is updated discretely during the local optimization process, and a variable learning factor is introduced. The memetic information of individual frogs is optimized with a threshold-selection strategy that encourages leaping from weaker individuals to better ones. In global optimization, the search orientation of each memeplex is based on exchanging and recombining information with other memeplexes, and a multi-path routing idea is introduced to select the optimal path. Simulation results show that this method performs well in real time, significantly improves energy efficiency, and prolongs the network lifetime. [ABSTRACT FROM AUTHOR]

Neighborhood rough set model is considered as one of the effective granular computing models in dealing with numerical data. This model is now widely discussed in feature selection and rule learning. However, there is no theoretical analysis on the issue of neighborhood granularity selection, the influence of sampling resolution, test and misclassification costs on modeling. In this paper, we design an adaptive neighborhood rough set model according to data precision and develop a fast backtracking algorithm for neighborhood rough sets based cost-sensitive feature selection by considering the trade-off between test costs and misclassification costs. In the proposed model, the neighborhood granularity, based on the 3 σ rule of statistics, is adaptive to data precision that is described by the multi-level confidence of the feature subsets. Our experiments, thoroughly performed on 12 datasets, demonstrate the effectiveness of the model and the efficiency of the backtracking algorithm. [ABSTRACT FROM AUTHOR]

This paper develops a multivariable multiple-model adaptive control scheme for adaptive state feedback state tracking control of systems whose plant-model matching conditions are uncertain and parameters are unknown. To deal with the uncertainty of plant-model matching conditions needed for adaptive control, multiple reference model systems are employed to generate multiple parameter estimation and feedback control signals from which a most suitable control input is selected by a control switching mechanism designed using multiple estimation errors. Such a new multiple-model control design is based on an expanded control system parametrization which has the capacity to cover system structural uncertainties. Stability analysis and simulation results ensure and verify the desired adaptive control system stability and tracking performance. [ABSTRACT FROM AUTHOR]

Abstract: Electron Repulsion Integrals (ERIs) are a common bottleneck in ab initio computational chemistry. It is known that sorted/reordered execution of ERIs results in efficient SIMD/vector processing. This paper shows that reconfigurable computing and heterogeneous processor architectures can also benefit from a deliberate ordering of ERI tasks. However, realizing these benefits as net speedup requires a very rapid sorting mechanism. This paper presents two such mechanisms. Included in this study are analytical, simulation-based, and experimental benchmarking approaches to consider five use cases for ERI sorting, i.e. SIMD processing, reconfigurable computing, limited address spaces, instruction cache exploitation, and data cache exploitation. Specific consideration is given to existing cache-based processors, FPGAs, and the Cell Broadband Engine processor. It is proposed that the analyses conducted in this work should be built upon to aid the development of software autotuners which will produce efficient ab initio computational chemistry codes for a variety of computer architectures. [Copyright &y& Elsevier]

Abstract: In this paper we study some global bifurcations arising in the Puu’s oligopoly model when we assume that the producers do not adjust to the best reply but use an adaptive process to obtain at each step the new production. Such bifurcations cause the appearance of a pair of closed invariant curves, one attracting and one repelling, this latter being involved in the subcritical Neimark bifurcation of the Cournot equilibrium point. The aim of the paper is to highlight the relationship between the global bifurcations causing the appearance/disappearance of two invariant closed curves and the homoclinic connections of some saddle cycle, already conjectured in [Agliari A, Gardini L, Puu T. Some global bifurcations related to the appearance of closed invariant curves. Comput Math Simul 2005;68:201–19]. We refine the results obtained in such a paper, showing that the appearance/disappearance of closed invariant curves is not necessarily related to the existence of an attracting cycle. The characterization of the periodicity tongues (i.e. a region of the parameter space in which an attracting cycle exists) associated with a subcritical Neimark bifurcation is also discussed. [Copyright &y& Elsevier]

Electromagnetic formation flight utilizes electromagnetic forces to control the relative positions of satellites, and offers a promising alternative to a traditional propellant-based spacecraft formation flying due to no fuel expenditure. Since available electromagnetic forces generated on board are small, their efficient use is a challenging issue in the presence of saturation. This paper proposes a novel adaptive reaching law based sliding mode control for the trajectory tracking of electromagnetic formation flight with actuator saturation. The adaptive reaching law is characterized by a modulation function which combines a saturated term with a non-switching term. The saturated term makes the sliding variable converge as fast as possible under input saturation, while the non-switching term is used to guarantee a terminal tracking performance and remove chattering. Thus, the introduced modulation function enables a smooth transition of the reaching law between two terms and an adaptive decrease rate of sliding variable. The proposed control can achieve a fast convergence and offers the robustness against external disturbance with input saturation. Simulation results are given to demonstrate the performance of the presented method. [ABSTRACT FROM AUTHOR]

In this paper, we consider a leader-follower output consensus problem for a class of uncertain heterogeneous non-affine pure-feedback multi-agent systems (MASs) in the presence of time-delay items and input saturation restrictions under switching directed topologies. A distributed adaptive control scheme is constructed by combining Lyapunov–Krasovskii functionals, backstepping methods, neural networks (NNs), auxiliary systems, graph theory, the mean value theorem and the implicit function theory along with the dynamic surface control (DSC) technique. The key advantages of the designed control approach are that there is no requirement of precise knowledge about uncertain dynamics and time-delay items of individual agents and the computational burden can drastically be reduced by employing the DSC technique. Also, norms of unknown weight of neural networks are estimated online instead of weight vectors themselves. In theory, it can be proven that the output signals of follower agents can synchronize with the leader’s and all signals in the closed-loop system are cooperatively semiglobally uniformly ultimately bounded (CSUUB) by suitable choice of design parameters. Two simulation examples are presented to demonstrate the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Most current research on reconfigurable control system puts emphasis on reconfiguration for adapting to actuator failures. However, the reconfigurable control system is necessitated for spacecraft attitude takeover control in the application of capturing target spacecraft whose fuel is exhausted to extend its operational lifetime by supplying them propulsion, navigation and guidance services. In this scenario, the capture of target spacecraft by space manipulators will cause a large shift in the dynamics of the service spacecraft. Not only do the mass properties change, but also does the thruster configuration. The changes in the mass, center of mass and inertia of the combined spacecraft will cause changes in the equivalent force exerted by each thruster. In this paper, considering the changes of thruster configuration and the control reallocation, a reconfigurable control system is designed for spacecraft attitude takeover control in post-capture of target by space manipulators in order to adapt to changes in the mass properties. The unknown inertia properties of target spacecraft in the system constitute a formidable technical challenge for controller design. Therefore, a modified adaptive dynamic inverse controller is proposed to provide global asymptotic stability in the presence of model uncertainties and nonlinearities. Moreover, by the null-space intersections control reallocation method, the thrust forces of service spacecraft can be redistributed and satisfy some constraints. Numerical simulations validate the feasibility of reconfigurable spacecraft attitude takeover control with large center of mass shifts and unknown inertia properties. [ABSTRACT FROM AUTHOR]

This paper is concerned with quantile regression for single-index-coefficient regression models. A practical algorithm and the asymptotic properties of the proposed estimators are established. The performance of the proposed method is investigated through simulation studies and a real data example. [ABSTRACT FROM AUTHOR]

This paper investigated the adaptive fault-tolerant control problem for a class of switched Takagi–Sugeno fuzzy systems. The considered switched fuzzy systems contain unmeasured states and actuator failures. In the control design, a switched state observer is designed to estimate the unmeasured states. Under the parallel-distributed compensation (PDC) design scheme and using the online estimation of actuator faults, an adaptive fuzzy fault-tolerant control scheme is proposed, in which a compensation control term is introduced in order to reduce the effect of actuator faults. Utilizing the estimated states and a segmentation of state space, the switching law is constructed. The sufficient conditions for ensuring the asymptotic stability of the switched fuzzy systems are proposed and formulated in the form of linear matrix inequalities (LMIs). By using the single Lyapunov function method, it is proved that the proposed control approach can guarantee the asymptotic stability of the closed-loop system. One practical example and a numerical example are given to illustrate the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

In this paper, adaptive stochastic resonance in time-delayed Newman–Watts small-world neuronal networks is studied, where the strength of synaptic connections between neurons is adaptively modulated by spike-timing-dependent plasticity (STDP). Numerical results show that, in the absence of information transmission delay, the phenomenon of stochastic resonance occurs and the efficiency of networked stochastic resonance can be slightly depressed by STDP. Due to the reduction of strong couplings induced by STDP, for a larger adjusting rate of STDP, a smaller peak value of the resonance response is obtained. In addition, the effect of stochastic resonance can be either promoted or destroyed by time delay, and multiple stochastic resonances appear intermittently at the integer multiples of periods of the subthreshold forcing. Furthermore, it is demonstrated that the networked stochastic resonance can also be dramatically affected by the small-world topology. For small and moderate adjusting rate of STDP, fine-tuning of the probability of adding links can significantly enhance the effect of stochastic resonance in adaptive neural network. Additionally, there is an optimal probability of adding links by which the noise-induced transmission of weak periodic signal peaks and the location of this span depends largely on the time delay and adjusting rate. [ABSTRACT FROM AUTHOR]

The network-constrained criterion is one of the fundamental variable selection models for high-dimensional data with correlated features. It is distinguished from others in that it can select features and simultaneously encourage global smoothness of the coefficients over the network via penalizing the weighted sum of squares of the scaled difference of the coefficients between neighbor vertices. However, because more features were selected while it was applied for the process of analysis of the “ China Stock Market Financial Database—Financial Ratios” , the so-called adaptive network-constrained criterion was proposed to tackle the problem via assigning various weights to the lasso penalty. Similar to the adaptive lasso, the proposed model enjoys consistency in variable selection if the weights have been given correctly in advance. The simulations show that the proposed model performed better than the other variable selection techniques mentioned in the paper with regards to model fitting; meanwhile, it selected fewer features than the network-constrained criterion. Furthermore, the mean value of the cross-validation likelihood and the number of selected features are tested to be accurate enough for practical applications. [ABSTRACT FROM AUTHOR]

Learner models are built to offer personalised solutions related to learning. They are often developed in parallel to the development of adaptive learning systems and thus, linked to the system’s development. The adaptive learning systems literature reports numerous accounts of learner model development, but there are no reports on the methodological aspects of developing learner models and the relation between the development of the learner model component and the rest of the system. This paper presents the Participatory Learner Modelling Design methodology, which outlines the steps for learner model development and their relation to the development of the system. The methodology is illustrated with a case study of an adaptive educational system. [ABSTRACT FROM AUTHOR]

This paper investigates adaptive cluster general projective synchronization (CGPS) of complex dynamic networks in finite time. Based on the finite time synchronization control techniques and Lyapunov stability theorem, sufficient conditions are derived to guarantee the realization of adaptive cluster general projective synchronization. Finally, numerical simulation is provided to support the theoretical results. [ABSTRACT FROM AUTHOR]

We call the intercept of the price function with the vertical axis the maximum price and the slope of the price function the marginal price . In this paper it is assumed that a monopolistic firm has full information about the marginal price and its own cost function but is uncertain on the maximum price. However, by repeated interaction with the market, the obtained price observations give a basis for an adaptive learning process of the maximum price. It is also assumed that the price observations have fixed delays, so the learning process can be described by a delayed differential equation. In the cases of one or two delays, the asymptotic behavior of the resulting dynamic process is examined, stability conditions are derived. Three main results are demonstrated in the two delay learning processes. First, it is possible to stabilize the equilibrium which is unstable in the one delay model. Second, complex dynamics involving chaos, which is impossible in the one delay model, can emerge. Third, alternations of stability and instability (i.e., stability switches) occur repeatedly. [ABSTRACT FROM AUTHOR]

In this paper, a new optimal distributed synchronization control scheme for the consensus problem of heterogeneous multi-agent differential graphical games is developed by iterative adaptive dynamic programming (ADP). The main idea is to use iterative ADP technique to obtain the iterative control law which makes all the agents track a given dynamics and simultaneously makes the iterative value function reach the Nash equilibrium. In the developed heterogeneous multi-agent differential graphical games, the agent of each node is different from one another. The dynamics and performance index function for each node depend only on local neighborhood information. A cooperative policy iteration algorithm is presented to achieve the optimal distributed synchronization control law for the agent of each node, where the coupled Hamilton–Jacobi equations for optimal synchronization control of heterogeneous multi-agent differential games can be avoided. Convergence analysis is developed to show that the iterative value functions of heterogeneous multi-agent differential graphical games can converge to the Nash equilibrium. Two simulation examples are given to show the effectiveness of the developed optimal control scheme. [ABSTRACT FROM AUTHOR]

In this paper, centralized and decentralized adaptive fuzzy output feedback control schemes are investigated for a class of stochastic nonlinear interconnected large-scale systems with dynamic uncertainties and unmeasured states. Fuzzy systems are used to approximate the unknown nonlinear functions. Decentralized K-filters are designed to estimate the unmeasured states. An available dynamic signal is introduced to dominate the unmodeled dynamics. By combining dynamic surface control (DSC) technique with backstepping design, the condition in which the approximation errors are assumed to be bounded is avoided. Using the defined compact set in the stability analysis, the unknown smooth interconnections and black box functions are effectively dealt with. Using It o ˆ formula and Chebyshev’s inequality, it is shown that all the signals in the closed-loop system are bounded in probability, and the error signals are semi-globally uniformly ultimately bounded in mean square or the sense of four-moment. Simulation results demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we study two types of synchronization between two coupled networks with interactions, including inner synchronization inside each network and outer synchronization between two networks with the adaptive controllers. By Barbalat׳s lemma and linear matrix inequality (LMI), we obtain a sufficient condition for each network to be asymptotic stability in terms of LMI. When the inner synchronization happens inside each network, while outer synchronization does not appear, we design the adaptive controllers to realize the inner and outer synchronization simultaneously, and investigate the identical or nonidentical coupling and interactive matrices. We then obtain a theorem for the outer synchronization with the adaptive controllers. Finally we give some numerical examples to show the effectiveness of our obtained results. Our findings would provide insights into the dynamics of coupled networks with diverse connections. [ABSTRACT FROM AUTHOR]

The cuckoo search algorithm (CS) is a simple and effective global optimization algorithm. It has been applied to solve a wide range of real-world optimization problem. In this paper, the proposed method uses two new mutation rules based on the rand and best individuals among the entire population. In order to balance the exploitation and exploration of the algorithm, the new rules are combined through a linear decreasing probability rule. Then, self adaptive parameter setting is introduced as a uniform random value to enhance the diversity of the population based on the relative success number of the proposed two new parameters in the previous period. To verify the performance of SACS, 16 benchmark functions chosen from literature are employed. Experimental results indicate that the proposed method performs better than, or at least comparable to state-of-the-art methods from literature when considering the quality of the solutions obtained. In the last part, experiments have been conducted on Lorenz system and Chen system to estimate the parameters of these two chaotic systems. Simulation results further demonstrate the proposed method is very effective. [ABSTRACT FROM AUTHOR]

This paper aims to develop a fractional-order model and a primal–dual algorithm for image denoising, where a regularization parameter can be adjusted adaptively according to Morozov discrepancy principle at each iteration to ensure that the denoised image retains in a specific set. In the light of saddle-point theory, the convergence of our proposed algorithm is guaranteed. Simulations with comparisons are carried out to demonstrate the effectiveness of our proposed algorithm for image denoising. [ABSTRACT FROM AUTHOR]

Recognizing faces corresponding to target individuals remains a challenging problem in video surveillance. Face recognition (FR) systems are exposed to videos captured under various operating conditions, and, since data distributions change over time, face captures diverge w.r.t. stored facial models. Although these models may be adapted when new reference videos become available, incremental learning with faces captured under different conditions may lead to knowledge corruption. This paper presents an adaptive multi-classifier system (AMCS) for video-to-video FR in changing surveillance environments. During enrolment, faces captured in reference videos are employed to design an individual-specific classifier. During operations, a tracker allows to regroup facial captures for individuals in the scene, and accumulate the predictions per track for robust spatiotemporal FR. Given a new reference video, the corresponding facial model is adapted according to the type of concept change. If a gradual pattern of change is detected, the individual-specific classifier(s) are adapted through incremental learning. To preserve knowledge, another classifier is learned and combined with the individuals previously-trained classifier(s) if an abrupt change is detected. For proof-of-concept, the performance of a particular implementation of this AMCS is assessed using videos from the Faces in Action dataset. By adapting facial models according to changes detected in new reference videos, this AMCS allows to sustain a high level of accuracy comparable to the same system that is always updated using a learn-and-combine approach, while reducing time and memory complexity. It also provides higher accuracy than incremental learning classifiers that suffer the effects of knowledge corruption. [ABSTRACT FROM AUTHOR]

Petri nets and Fuzzy Petri nets as modeling formalism are not adaptable according to the changes of the arc weight. Weights are the parameters that represent the new incoming data of the system modeled by a (Fuzzy) Petri net. The weight changes meet the system changes in a variety of application domains (e.g. Intelligent E-learning, Computer Numerical Control, Weather Forecasting, and Expert Systems). In this paper, we introduce a new class of Fuzzy Petri nets that takes into account the weight changes of the arc in the Fuzzy reasoning process. We give the formal description of the model, an algorithm for learning the weights without the need to transfer into a neural network, an algorithm for the fuzzy reasoning, the algebraic forms of the new state equation, and the stability analysis. Finally, we introduce a weather forecasting application as a real example. [ABSTRACT FROM AUTHOR]

This paper proposes a novel fault estimation observer for both nonlinear descriptor and normal systems, which are subjected simultaneously to actuator faults, sensor faults and unstructured non-parametric uncertainties. The considered faults can be unbounded and can have many forms, such as abrupt faults, constant faults and high-frequency faults. Sufficient conditions for the existence of the proposed observer with an H ∞ performance have been derived based on Lyapunov stability theory, and expressed in terms of Bilinear Matrix Inequalities (BMIs) which are then converted into quasi-convex Linear Matrix Inequalities (LMIs) by using the cone complementarity linearization (CCL) algorithm. Simulated examples are presented to illustrate the effectiveness of the proposed observer for both descriptor systems and normal systems and have been shown to perform better than some of the existing methods of fault estimation. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new postprocessing algorithm for JPEG compressed images considering a compressed domain lossless data hiding scheme. The conventional compressed domain lossless data hiding scheme can embed information bits into the bitstream, while maintaining the file size and JPEG compatibility. As a new application of the conventional scheme, we design an algorithm of embedding filter coefficients in the bitstream, such that the embedded filter can be used to enhance the quality of the decoded image. To this end, a Wiener filter is estimated between the original and decoded images, and the coefficients are embedded into the JPEG bitstream. Moreover, the size, symmetry, and precision of the filter and the number of filters are determined under the constraint of embeddable bits. The proposed JPEG decoder extracts the filter coefficients from the bitstream and enhances the quality of the decoded image by applying the Wiener filter. The proposed algorithm can also work with conventional JPEG postprocessing algorithms, so that the performance of such conventional algorithms can be further improved. [ABSTRACT FROM AUTHOR]