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Feng Ding - One of the best experts on this subject based on the ideXlab platform.
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A New Iterative Least Squares Parameter Estimation Approach for Equation-error Autoregressive Systems
International Journal of Control Automation and Systems, 2019Co-Authors: Lijuan Wan, Feng Ding, Ximei Liu, Chunping ChenAbstract:This paper investigates the identification methods for controlled autoregressive systems with autoregressive noise (i.e., equation-error autoregressive systems) from given input and output data. By applying the iterative technique and the hierarchical identification principle, an iterative least squares identification algorithm is presented and a recursive generalized least squares algorithm is given for comparison. The basic idea is to replace the unknown noise terms in the Information Vector with their estimated residuals. The simulation test results show the effectiveness of these algorithms.
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Maximum likelihood based identification methods for rational models
International Journal of Systems Science, 2019Co-Authors: Jing Chen, Feng Ding, Quanmin Zhu, Yanjun LiuAbstract:ABSTRACTIn a rational model, some terms of the Information Vector are correlated with the noise, which makes the traditional least squares based iterative algorithms biased. In order to overcome th...
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Least Squares based Iterative Parameter Estimation Algorithm for Stochastic Dynamical Systems with ARMA Noise Using the Model Equivalence
International Journal of Control Automation and Systems, 2018Co-Authors: Feng Ding, Dandan Meng, Jiyang Dai, Qishen Li, Ahmed Alsaedi, Tasawar HayatAbstract:By means of the model equivalence theory, this paper proposes a model equivalence based least squares iterative algorithm for estimating the parameters of stochastic dynamical systems with ARMA noise. The proposed algorithm reduces the number of the unknown noise terms in the Information Vector and can give more accurate parameter estimates compared with the generalized extended least squares algorithm. The validity of the proposed method is evaluated through a numerical example.
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Combined state and parameter estimation for Hammerstein systems with time-delay using the Kalman filtering
International Journal of Adaptive Control and Signal Processing, 2017Co-Authors: Feng Ding, Weili Xiong, Erfu YangAbstract:This paper discusses the state and parameter estimation problem for a class of Hammerstein state space systems with time-delay. Both the process noise and the measurement noise are considered in the system. Based on the observable canonical state space form and the key term separation, a pseudo-linear regressive identification model is obtained. For the unknown states in the Information Vector, the Kalman filter is used to search for the optimal state estimates. A Kalman-filter based least squares iterative and a recursive least squares algorithms are proposed. Extending the Information Vector to include the latest Information terms which are missed for the time-delay, the Kalman-filter based recursive extended least squares algorithm is derived to obtain the estimates of the unknown time-delay, parameters and states. The numerical simulation results are given to illustrate the effectiveness of the proposed algorithms.
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Convergence Analysis of the Hierarchical Least Squares Algorithm for Bilinear-in-Parameter Systems
Circuits Systems and Signal Processing, 2016Co-Authors: Xuehai Wang, Feng Ding, Fuad E. Alsaadi, Tasawar HayatAbstract:This paper studies the convergence of the hierarchical identification algorithm for bilinear-in-parameter systems. By replacing the unknown variables in the Information Vector with their estimates, a hierarchical least squares algorithm is derived based on the model decomposition. The proposed algorithm has higher computational efficiency than the over-parameterization model-based recursive least squares algorithm. The performance analysis shows that the parameter estimation errors converge to zero under persistent excitation conditions. The effectiveness of the proposed algorithm is verified by simulation examples.
Feng Pan - One of the best experts on this subject based on the ideXlab platform.
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Gradient-based iterative identification for Wiener nonlinear systems with non-uniform sampling
Nonlinear Dynamics, 2013Co-Authors: Lincheng Zhou, Feng PanAbstract:This paper focuses on the identification problem of Wiener nonlinear systems with non-uniform sampling. The mathematical model for the Wiener nonlinear system is established from the non-uniformly sampled input–output data. In order to solve the identification problem of the Wiener nonlinear system with the unmeasurable variables in the Information Vector, the gradient-based iterative algorithm is presented by replacing the unmeasurable variables with their corresponding iterative estimates. Finally, the simulation results indicate that the proposed algorithm is effective.
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Least-Squares-Based Iterative Identification Algorithm for Wiener Nonlinear Systems
Journal of Applied Mathematics, 2013Co-Authors: Lincheng Zhou, Feng PanAbstract:This paper focuses on the identification problem of Wiener nonlinear systems. The application of the key-term separation principle provides a simplified form of the estimated parameter model. To solve the identification problem of Wiener nonlinear systems with the unmeasurable variables in the Information Vector, the least-squares-based iterative algorithm is presented by replacing the unmeasurable variables in the Information Vector with their corresponding iterative estimates. The simulation results indicate that the proposed algorithm is effective.
Lincheng Zhou - One of the best experts on this subject based on the ideXlab platform.
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ICCAIS - Levenberg-Marquardt iterative algorithm for Hammerstein nonlinear systems
2015 International Conference on Control Automation and Information Sciences (ICCAIS), 2015Co-Authors: Lincheng Zhou, Peiyi ZhuAbstract:In this paper, we study the identification problem of Hammerstein nonlinear systems. A Levenberg-Marquardt iterative (LMI) algorithm is developed for Hammerstein nonlinear systems. The basic idea is to establish a Hammerstein nonlinear model by means of the key-term separation principle and then derive the LMI algorithm by replacing the unmeasurable variables in the Information Vector with their corresponding iterative estimates for the proposed model. Finally, the simulation results show the effectiveness of the LMI algorithm.
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Gradient-based iterative identification for Wiener nonlinear systems with non-uniform sampling
Nonlinear Dynamics, 2013Co-Authors: Lincheng Zhou, Feng PanAbstract:This paper focuses on the identification problem of Wiener nonlinear systems with non-uniform sampling. The mathematical model for the Wiener nonlinear system is established from the non-uniformly sampled input–output data. In order to solve the identification problem of the Wiener nonlinear system with the unmeasurable variables in the Information Vector, the gradient-based iterative algorithm is presented by replacing the unmeasurable variables with their corresponding iterative estimates. Finally, the simulation results indicate that the proposed algorithm is effective.
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Least-Squares-Based Iterative Identification Algorithm for Wiener Nonlinear Systems
Journal of Applied Mathematics, 2013Co-Authors: Lincheng Zhou, Feng PanAbstract:This paper focuses on the identification problem of Wiener nonlinear systems. The application of the key-term separation principle provides a simplified form of the estimated parameter model. To solve the identification problem of Wiener nonlinear systems with the unmeasurable variables in the Information Vector, the least-squares-based iterative algorithm is presented by replacing the unmeasurable variables in the Information Vector with their corresponding iterative estimates. The simulation results indicate that the proposed algorithm is effective.
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Recursive Least-Squares Estimation for Hammerstein Nonlinear Systems with Nonuniform Sampling
Mathematical Problems in Engineering, 2013Co-Authors: Lincheng Zhou, Ruifeng Ding, Jie ShengAbstract:This paper focuses on the identification problem of Hammerstein nonlinear systems with nonuniform sampling. Using the key-term separation principle, we present a discrete identification model with nonuniform sampling input and output data based on the frame period. To estimate parameters of the presented model, an auxiliary model-based recursive least-squares algorithm is derived by replacing the unmeasurable variables in the Information Vector with their corresponding recursive estimates. The simulation results show the effectiveness of the proposed algorithm.
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Least-squares-based iterative identification algorithm for Hammerstein nonlinear systems with non-uniform sampling
International Journal of Computer Mathematics, 2013Co-Authors: Ruifeng Ding, Lincheng ZhouAbstract:This paper focuses on identification problems for Hammerstein systems with non-uniform sampling. By using the over-parameterization technique, we derive a linear regressive identification model with different input updating rates. To solve the identification problem of Hammerstein output error systems with the unmeasurable variables in the Information Vector, the least-squares-based iterative algorithm is presented by replacing the unmeasurable variables with their corresponding iterative estimates. The performances of the proposed algorithm are analysed and compared by using a numerical example.
Jing Chen - One of the best experts on this subject based on the ideXlab platform.
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Maximum likelihood based identification methods for rational models
International Journal of Systems Science, 2019Co-Authors: Jing Chen, Feng Ding, Quanmin Zhu, Yanjun LiuAbstract:ABSTRACTIn a rational model, some terms of the Information Vector are correlated with the noise, which makes the traditional least squares based iterative algorithms biased. In order to overcome th...
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A novel maximum likelihood-based stochastic gradient algorithm for Hammerstein nonlinear systems with coloured noise
International Journal of Modelling Identification and Control, 2019Co-Authors: Jing ChenAbstract:This paper proposes a novel maximum likelihood based stochastic gradient algorithm for Hammerstein nonlinear systems with coloured noise. The unknown noises in the Information Vector are replaced by their estimates, and then the parameters can be obtained by using the proposed algorithm through the noise estimates. Compared with the maximum likelihood-based recursive least squares algorithm, the proposed algorithm has less computation burden. Furthermore, the performance of the proposed algorithm is analysed and compared using a simulation example.
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An auxiliary model based multi-innovation recursive least squares estimation algorithms for MIMO Hammerstein system
2011Co-Authors: Xiuping Wang, Jing ChenAbstract:An auxiliary model based multi-innovation recursive least squares estimation algorithms is proposed in this paper. The unknown variables in the Information Vector can be estimated by using the auxiliary model. The proposed recursive least squares algorithm uses not only the current innovation but also the past innovations at each recursion and thus the parameter estimation accuracy can be improved. Finally, the simulation results indicate that the proposed algorithm has good performances.
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LSMS/ICSEE (1) - Multi-innovation Generalized Extended Stochastic Gradient Algorithm for Multi-Input Multi-Output Nonlinear Box-Jenkins Systems Based on the Auxiliary Model
Lecture Notes in Computer Science, 2010Co-Authors: Jing Chen, Xiuping WangAbstract:An auxiliary model based multi-innovation generalized extended stochastic gradient algorithm is developed for multivariable nonlinear Box-Jenkins systems. The basic idea is to construct an auxiliary model using the measured data and to replace the unknown terms in the Information Vector with their estimates, i.e., the outputs of the auxiliary model. The proposed algorithm can give high accurate parameter estimation compared with existing stochastic gradient algorithms. A simulation example is given.
Tongwen Chen - One of the best experts on this subject based on the ideXlab platform.
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auxiliary model based least squares identification methods for hammerstein output error systems
Systems & Control Letters, 2007Co-Authors: Feng Ding, Tongwen ChenAbstract:Abstract The difficulty in identification of a Hammerstein (a linear dynamical block following a memoryless nonlinear block) nonlinear output-error model is that the Information Vector in the identification model contains unknown variables—the noise-free (true) outputs of the system. In this paper, an auxiliary model-based least-squares identification algorithm is developed. The basic idea is to replace the unknown variables by the output of an auxiliary model. Convergence analysis of the algorithm indicates that the parameter estimation error consistently converges to zero under a generalized persistent excitation condition. The simulation results show the effectiveness of the proposed algorithms.
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Performance analysis of estimation algorithms of nonstationary ARMA processes
IEEE Transactions on Signal Processing, 2006Co-Authors: Feng Ding, Yang Shi, Tongwen ChenAbstract:The correlation analysis based methods are not suitable for identifying parameters of nonstationary autoregressive (AR), moving average (MA), and ARMA systems. By using estimation residuals in place of unmeasurable noise terms in Information Vector or matrix, we develop a least squares based and gradient based algorithms and establish the consistency of the proposed algorithms without assuming noise stationarity, ergodicity, or existence of higher order moments. Furthermore, we derive the conditions for convergence of the parameter estimation. The simulation results validate the convergence theorems proposed.
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Least squares identification of non-stationary MA systems
Proceedings of the 2005 American Control Conference 2005., 1Co-Authors: Feng Ding, Yang Shi, Tongwen ChenAbstract:The correlation analysis based methods are not suitable for identifying parameters of non-stationary MA systems, for which two algorithms are developed, an iterative and a recursive multi-innovation least squares ones. The basic idea is to replace immeasurable noise terms in the Information Vector by the estimation residuals, which are computed also according to the parameter estimates. This is a hierarchical computation process. Furthermore, the conditions of convergence of the parameter estimation by the recursive algorithm are derived. The simulation results validate the algorithms proposed.