The Experts below are selected from a list of 16356 Experts worldwide ranked by ideXlab platform
Feng Ding - One of the best experts on this subject based on the ideXlab platform.
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data filtering based recursive identification for an exponential autoregressive moving average model by using the multi innovation theory
Iet Control Theory and Applications, 2020Co-Authors: Feng Ding, Ahmed Alsaedi, Tasawar HayatAbstract:This study employs the data filtering technique to investigate the recursive identification problems for a non-linear exponential autoregressive model with moving average noise, i.e. the ExpARMA model. Whitening the ExpARMA model by a linear filter, the original identification model is divided into a filtered identification model and a coloured noise model, then a filtering-based extended Stochastic Gradient Algorithm is derived. In order to improve the parameter estimation accuracy, the multi-innovation identification theory is used to develop a filtering-based multi-innovation extended Stochastic Gradient Algorithm for the ExpARMA model. A simulation example is given to demonstrate the superiority of the proposed filtering-based multi-innovation Algorithm over the existing Algorithms.
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recursive coupled projection Algorithms for multivariable output error like systems with coloured noises
Iet Signal Processing, 2020Co-Authors: Jian Pan, Feng Ding, Xiao Zhang, Qinyao Liu, Yufang Chang, Jie ShengAbstract:By combining the coupling identification concept with the Gradient search, this study develops a partially coupled generalised extended projection Algorithm and a partially coupled generalised extended Stochastic Gradient Algorithm to estimate the parameters of a multivariable output-error-like system with autoregressive moving average noise from input–output data. The key is to divide the identification model into several submodels based on the hierarchical identification principle and to establish the parameter estimation Algorithm by using the coupled relationship between these submodels. The simulation test results indicate that the proposed Algorithms are effective.
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data filtering based maximum likelihood Gradient estimation Algorithms for a multivariate equation error system with arma noise
Journal of The Franklin Institute-engineering and Applied Mathematics, 2020Co-Authors: Lijuan Liu, Feng Ding, Haibo Liu, Ahmed AlsaediAbstract:Abstract In this paper, we use the maximum likelihood principle and the data filtering technique to study the identification issue of the multivariate equation-error system whose outputs are contaminated by an ARMA noise process. The key is to break the system into several regressive identification subsystems based on the number of the outputs. Then a multivariate equation-error subsystem is transformed into a filtered model and a filtered noise model, and a filtering based maximum likelihood extended Stochastic Gradient Algorithm is derived to estimate the parameters of these two models. The filtering based maximum likelihood extended Stochastic Gradient Algorithm has higher parameter estimation accuracy than the maximum likelihood generalized extended Stochastic Gradient Algorithm and the maximum likelihood recursive generalized extended least squares Algorithm. The simulation examples indicate that the proposed methods work well.
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two stage multi innovation Stochastic Gradient Algorithm for multivariate output error arma systems based on the auxiliary model
International Journal of Systems Science, 2019Co-Authors: Feng Ding, Tasawar HayatAbstract:ABSTRACTThis paper investigates the parameter estimation problem for multivariate output-error systems perturbed by autoregressive moving average noises. Since the identification model has two diff...
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recursive least squares Algorithm and Stochastic Gradient Algorithm for feedback nonlinear equation error systems
International Journal of Modelling Identification and Control, 2019Co-Authors: Guanglei Song, Feng DingAbstract:Many industrial systems exhibit the nonlinear characteristics. Generally, the structure of the system is taken by feedback closed-loop for the purpose of realising the automatic control of industrial processes. Therefore, the industrial systems are the closed-loop feedback nonlinear systems which have complicated structures. The mathematical models of systems provide the support and basis for the design of the control system and the better control performance. However, it is hard to determine the models of closed-loop feedback nonlinear systems due to the complex structures. The goal of this study is to develop an identification way for a feedback nonlinear system including a forward channel and a feedback channel, where the forward channel is described by a controlled autoregressive model and the feedback channel takes the form of a static nonlinear function. By taking advantage of the least squares optimisation, a recursive least squares Algorithm is established and shows its good performance to solve the identification problem for the feedback nonlinear system.
Tasawar Hayat - One of the best experts on this subject based on the ideXlab platform.
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data filtering based recursive identification for an exponential autoregressive moving average model by using the multi innovation theory
Iet Control Theory and Applications, 2020Co-Authors: Feng Ding, Ahmed Alsaedi, Tasawar HayatAbstract:This study employs the data filtering technique to investigate the recursive identification problems for a non-linear exponential autoregressive model with moving average noise, i.e. the ExpARMA model. Whitening the ExpARMA model by a linear filter, the original identification model is divided into a filtered identification model and a coloured noise model, then a filtering-based extended Stochastic Gradient Algorithm is derived. In order to improve the parameter estimation accuracy, the multi-innovation identification theory is used to develop a filtering-based multi-innovation extended Stochastic Gradient Algorithm for the ExpARMA model. A simulation example is given to demonstrate the superiority of the proposed filtering-based multi-innovation Algorithm over the existing Algorithms.
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two stage multi innovation Stochastic Gradient Algorithm for multivariate output error arma systems based on the auxiliary model
International Journal of Systems Science, 2019Co-Authors: Feng Ding, Tasawar HayatAbstract:ABSTRACTThis paper investigates the parameter estimation problem for multivariate output-error systems perturbed by autoregressive moving average noises. Since the identification model has two diff...
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fitting the exponential autoregressive model through recursive search
Journal of The Franklin Institute-engineering and Applied Mathematics, 2019Co-Authors: Huan Xu, Feng Ding, Ahmed Alsaedi, Tasawar HayatAbstract:Abstract This paper focuses on the recursive parameter estimation methods for the exponential autoregressive (ExpAR) model. Applying the negative Gradient search and introducing a forgetting factor, a Stochastic Gradient and a forgetting factor Stochastic Gradient Algorithms are presented. In order to improve the parameter estimation accuracy and the convergence rate, the multi-innovation identification theory is employed to derive a forgetting factor multi-innovation Stochastic Gradient Algorithm. A simulation example is provided to test the effectiveness of the proposed Algorithms.
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a multi innovation state and parameter estimation Algorithm for a state space system with d step state delay
Signal Processing, 2017Co-Authors: Feng Ding, Ahmed Alsaedi, Tasawar HayatAbstract:Abstract This paper considers the state and parameter estimation problem of a state-delay system. On the basis of the Stochastic Gradient Algorithm (i.e., the Gradient based search estimation Algorithm), this work extends the scalar innovation into an innovation vector and presents a multi-innovation Gradient parameter estimation Algorithm for a state-space system with d-step state-delay by means of the multi-innovation identification theory. For thesystems whose states are unknown, we use the states of the state observer for the parameter estimation and use the estimated parameters for the state estimation. This forms a joint multi-innovation state and parameter estimation Algorithm for the state-delay systems with immeasurable states. The simulation results indicate that the proposed Algorithms can work well.
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recursive parameter identification of the dynamical models for bilinear state space systems
Nonlinear Dynamics, 2017Co-Authors: Xiao Zhang, Feng Ding, Tasawar Hayat, Fuad E AlsaadiAbstract:This paper investigates the recursive parameter and state estimation Algorithms for a special class of nonlinear systems (i.e., bilinear state space systems). A state observer-based Stochastic Gradient (O-SG) Algorithm is presented for the bilinear state space systems by using the Gradient search. In order to improve the parameter estimation accuracy and the convergence rate of the O-SG Algorithm, a state observer-based multi-innovation Stochastic Gradient Algorithm and a state observer-based recursive least squares identification Algorithm are derived by means of the multi-innovation theory. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed Algorithms.
Ahmed Alsaedi - One of the best experts on this subject based on the ideXlab platform.
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data filtering based recursive identification for an exponential autoregressive moving average model by using the multi innovation theory
Iet Control Theory and Applications, 2020Co-Authors: Feng Ding, Ahmed Alsaedi, Tasawar HayatAbstract:This study employs the data filtering technique to investigate the recursive identification problems for a non-linear exponential autoregressive model with moving average noise, i.e. the ExpARMA model. Whitening the ExpARMA model by a linear filter, the original identification model is divided into a filtered identification model and a coloured noise model, then a filtering-based extended Stochastic Gradient Algorithm is derived. In order to improve the parameter estimation accuracy, the multi-innovation identification theory is used to develop a filtering-based multi-innovation extended Stochastic Gradient Algorithm for the ExpARMA model. A simulation example is given to demonstrate the superiority of the proposed filtering-based multi-innovation Algorithm over the existing Algorithms.
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data filtering based maximum likelihood Gradient estimation Algorithms for a multivariate equation error system with arma noise
Journal of The Franklin Institute-engineering and Applied Mathematics, 2020Co-Authors: Lijuan Liu, Feng Ding, Haibo Liu, Ahmed AlsaediAbstract:Abstract In this paper, we use the maximum likelihood principle and the data filtering technique to study the identification issue of the multivariate equation-error system whose outputs are contaminated by an ARMA noise process. The key is to break the system into several regressive identification subsystems based on the number of the outputs. Then a multivariate equation-error subsystem is transformed into a filtered model and a filtered noise model, and a filtering based maximum likelihood extended Stochastic Gradient Algorithm is derived to estimate the parameters of these two models. The filtering based maximum likelihood extended Stochastic Gradient Algorithm has higher parameter estimation accuracy than the maximum likelihood generalized extended Stochastic Gradient Algorithm and the maximum likelihood recursive generalized extended least squares Algorithm. The simulation examples indicate that the proposed methods work well.
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fitting the exponential autoregressive model through recursive search
Journal of The Franklin Institute-engineering and Applied Mathematics, 2019Co-Authors: Huan Xu, Feng Ding, Ahmed Alsaedi, Tasawar HayatAbstract:Abstract This paper focuses on the recursive parameter estimation methods for the exponential autoregressive (ExpAR) model. Applying the negative Gradient search and introducing a forgetting factor, a Stochastic Gradient and a forgetting factor Stochastic Gradient Algorithms are presented. In order to improve the parameter estimation accuracy and the convergence rate, the multi-innovation identification theory is employed to derive a forgetting factor multi-innovation Stochastic Gradient Algorithm. A simulation example is provided to test the effectiveness of the proposed Algorithms.
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a multi innovation state and parameter estimation Algorithm for a state space system with d step state delay
Signal Processing, 2017Co-Authors: Feng Ding, Ahmed Alsaedi, Tasawar HayatAbstract:Abstract This paper considers the state and parameter estimation problem of a state-delay system. On the basis of the Stochastic Gradient Algorithm (i.e., the Gradient based search estimation Algorithm), this work extends the scalar innovation into an innovation vector and presents a multi-innovation Gradient parameter estimation Algorithm for a state-space system with d-step state-delay by means of the multi-innovation identification theory. For thesystems whose states are unknown, we use the states of the state observer for the parameter estimation and use the estimated parameters for the state estimation. This forms a joint multi-innovation state and parameter estimation Algorithm for the state-delay systems with immeasurable states. The simulation results indicate that the proposed Algorithms can work well.
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Gradient based recursive identification methods for input nonlinear equation error closed loop systems
Circuits Systems and Signal Processing, 2017Co-Authors: Bingbing Shen, Feng Ding, Ahmed Alsaedi, Tasawar HayatAbstract:The identification problem of closed-loop or feedback nonlinear systems is a hot topic. Based on the hierarchical identification principle, this paper presents a hierarchical Stochastic Gradient Algorithm and a hierarchical multi-innovation Stochastic Gradient Algorithm for feedback nonlinear systems. The simulation results show that the hierarchical multi-innovation Stochastic Gradient can more effectively estimate the parameters of the feedback nonlinear systems than the hierarchical Stochastic Gradient Algorithm.
Xuehai Wang - One of the best experts on this subject based on the ideXlab platform.
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hierarchical Stochastic Gradient Algorithm and its performance analysis for a class of bilinear in parameter systems
Circuits Systems and Signal Processing, 2017Co-Authors: Feng Ding, Xuehai WangAbstract:This paper considers the parameter identification for a special class of nonlinear systems, i.e., bilinear-in-parameter systems. Based on the hierarchical identification principle, a hierarchical Stochastic Gradient (HSG) estimation Algorithm is presented. The basic idea is to decompose a bilinear-in-parameter system into two subsystems and to derive the HSG identification Algorithm for estimating the system parameters by replacing the unknown variables in the information vectors with their estimates obtained at the previous time. The convergence analysis of the proposed Algorithm indicates that the parameter estimation errors converge to zero under persistent excitation conditions. The simulation results show that the proposed Algorithm is effective.
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recursive parameter and state estimation for an input nonlinear state space system using the hierarchical identification principle
Signal Processing, 2015Co-Authors: Xuehai Wang, Feng DingAbstract:This paper focuses on the identification problem of an input nonlinear state space system with colored noise. Based on the observability canonical form, an identification model is derived and a state observer is designed. By using the hierarchical identification principle, a state observer based hierarchical Stochastic Gradient Algorithm is presented for estimating the parameter vectors and states jointly. Furthermore, by using the multi-innovation identification theory, a state observer based hierarchical multi-innovation Stochastic Gradient Algorithm is proposed for improving the convergence rate. The analysis indicates that the parameter estimates given by the proposed Algorithms converge to the true values under persistent excitation conditions. Two numerical examples are offered to demonstrate the effectiveness of the proposed Algorithms. HighlightsThe identification problem for nonlinear state space systems is considered.A state observer based hierarchical multi-innovation Gradient Algorithm is presented.The proposed Algorithm can estimate the system states and parameters jointly.The convergence of the proposed Algorithm is studied.
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convergence of the auxiliary model based multi innovation generalized extended Stochastic Gradient Algorithm for box jenkins systems
Nonlinear Dynamics, 2015Co-Authors: Xuehai Wang, Feng DingAbstract:This paper focuses on the parameter estimation problem of Box–Jenkins systems. Using the multi-innovation identification theory, an auxiliary model-based multi-innovation generalized extended Stochastic Gradient Algorithm is derived. The convergence of the proposed Algorithm is analyzed based on the Stochastic martingale theory. It is proved that the parameter estimation errors converge to zero under persistent excitation conditions. Two simulation examples are provided to confirm the convergence results.
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performance analysis of the recursive parameter estimation Algorithms for multivariable box jenkins systems
Journal of The Franklin Institute-engineering and Applied Mathematics, 2014Co-Authors: Xuehai Wang, Feng DingAbstract:Abstract Two auxiliary model based recursive identification Algorithms, a generalized extended Stochastic Gradient Algorithm and a recursive generalized extended least squares Algorithm, are developed for multivariable Box–Jenkins systems. The basic idea is to use the auxiliary models to estimate the unknown noise-free outputs of the system and to replace the unmeasurable terms in the information vectors with their estimates. We prove that the estimation errors given by the proposed Algorithms converge to zero under the persistent excitation condition. Finally, an example is provided to show the effectiveness of the proposed Algorithms.
Jing Chen - One of the best experts on this subject based on the ideXlab platform.
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aitken based modified kalman filtering Stochastic Gradient Algorithm for dual rate nonlinear models
Journal of The Franklin Institute-engineering and Applied Mathematics, 2019Co-Authors: Jing Chen, Yong Zhang, Quanmin Zhu, Yanjun LiuAbstract:Abstract This paper develops an Aitken based modified Kalman filtering Stochastic Gradient Algorithm for dual-rate nonlinear models. The Aitken based method can increase the convergence rate and the modified Kalman filter can improve the estimation accuracy. Thus compared to the traditional auxiliary model based Stochastic Gradient Algorithm, the proposed Algorithm in this paper is more effective, and this is proved by the convergence analysis. Furthermore, two simulated examples are given to illustrate the effectiveness of the proposed Algorithm.
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Gradient based parameter estimation for input nonlinear systems with arma noises based on the auxiliary model
Nonlinear Dynamics, 2013Co-Authors: Jing Chen, Yan Zhang, Ruifeng DingAbstract:This paper presents a Gradient-based iterative identification Algorithm and an auxiliary-model-based multi-innovation generalized extended Stochastic Gradient Algorithm for input nonlinear systems with autoregressive moving average (ARMA) noises, i.e., the input nonlinear Box–Jenkins (IN–BJ) systems. The estimation errors given by the Gradient-based iterative Algorithm are smaller than the generalized extended Stochastic Gradient Algorithm under same data lengths. A simulation example is provided.
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parameter identification of systems with preload nonlinearities based on the finite impulse response model and negative Gradient search
Applied Mathematics and Computation, 2012Co-Authors: Jing Chen, Rui DingAbstract:Abstract This paper considers identification problems of a class of nonlinear systems. By introducing a switching function and the finite impulse response model, we obtain the identification model of a preload nonlinear system and develop a Stochastic Gradient Algorithm to the parameters of the system. Furthermore, a Stochastic Gradient Algorithm with a forgetting factor is given to improve the identification accuracy. A simulation results indicate that the forgetting factor can improve the parameter estimation accuracy.
