The Experts below are selected from a list of 21387 Experts worldwide ranked by ideXlab platform
Liang Hua - One of the best experts on this subject based on the ideXlab platform.
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parameter estimation algorithms for hammerstein output error systems using Levenberg Marquardt optimization method with varying interval measurements
Journal of The Franklin Institute-engineering and Applied Mathematics, 2017Co-Authors: Wei Xing Zheng, Liang HuaAbstract:This paper studies the parameter estimation problem of Hammerstein output error autoregressive (OEAR) systems. According to the maximum likelihood principle and the Levenberg–Marquardt optimization method, a maximum likelihood Levenberg–Marquardt recursive (ML-LM-R) algorithm using the varying interval input–output data is proposed. Furthermore, a stochastic gradient algorithm is also derived in order to compare it with the proposed ML-LM-R algorithm. Two numerical examples are provided to verify the effectiveness of the proposed algorithms.
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Parameter estimation algorithms for Hammerstein output error systems using Levenberg–Marquardt optimization method with varying interval measurements☆
Journal of the Franklin Institute, 2017Co-Authors: Li Junhong, Wei Xing Zheng, Liang HuaAbstract:This paper studies the parameter estimation problem of Hammerstein output error autoregressive (OEAR) systems. According to the maximum likelihood principle and the Levenberg–Marquardt optimization method, a maximum likelihood Levenberg–Marquardt recursive (ML-LM-R) algorithm using the varying interval input–output data is proposed. Furthermore, a stochastic gradient algorithm is also derived in order to compare it with the proposed ML-LM-R algorithm. Two numerical examples are provided to verify the effectiveness of the proposed algorithms.
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Parameter identification for Hammerstein nonlinear systems using the maximum likelihood principle and Levenberg-Marquardt optimization method
2016 35th Chinese Control Conference (CCC), 2016Co-Authors: Li Junhong, Wei Xing Zheng, Liang HuaAbstract:This paper studies the parameter estimation problems of Hammerstein output error autoregressive (OEAR) systems. A maximum likelihood Levenberg-Marquardt recursive (ML-LM-R) algorithm using the varying interval input-output data is presented by using the maximum likelihood principle and Levenberg-Marquardt optimization method. The effectiveness of the algorithm is verified by a numerical example.
Wei Xing Zheng - One of the best experts on this subject based on the ideXlab platform.
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parameter estimation algorithms for hammerstein output error systems using Levenberg Marquardt optimization method with varying interval measurements
Journal of The Franklin Institute-engineering and Applied Mathematics, 2017Co-Authors: Wei Xing Zheng, Liang HuaAbstract:This paper studies the parameter estimation problem of Hammerstein output error autoregressive (OEAR) systems. According to the maximum likelihood principle and the Levenberg–Marquardt optimization method, a maximum likelihood Levenberg–Marquardt recursive (ML-LM-R) algorithm using the varying interval input–output data is proposed. Furthermore, a stochastic gradient algorithm is also derived in order to compare it with the proposed ML-LM-R algorithm. Two numerical examples are provided to verify the effectiveness of the proposed algorithms.
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Parameter estimation algorithms for Hammerstein output error systems using Levenberg–Marquardt optimization method with varying interval measurements☆
Journal of the Franklin Institute, 2017Co-Authors: Li Junhong, Wei Xing Zheng, Liang HuaAbstract:This paper studies the parameter estimation problem of Hammerstein output error autoregressive (OEAR) systems. According to the maximum likelihood principle and the Levenberg–Marquardt optimization method, a maximum likelihood Levenberg–Marquardt recursive (ML-LM-R) algorithm using the varying interval input–output data is proposed. Furthermore, a stochastic gradient algorithm is also derived in order to compare it with the proposed ML-LM-R algorithm. Two numerical examples are provided to verify the effectiveness of the proposed algorithms.
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Parameter identification for Hammerstein nonlinear systems using the maximum likelihood principle and Levenberg-Marquardt optimization method
2016 35th Chinese Control Conference (CCC), 2016Co-Authors: Li Junhong, Wei Xing Zheng, Liang HuaAbstract:This paper studies the parameter estimation problems of Hammerstein output error autoregressive (OEAR) systems. A maximum likelihood Levenberg-Marquardt recursive (ML-LM-R) algorithm using the varying interval input-output data is presented by using the maximum likelihood principle and Levenberg-Marquardt optimization method. The effectiveness of the algorithm is verified by a numerical example.
Li Junhong - One of the best experts on this subject based on the ideXlab platform.
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Parameter estimation algorithms for Hammerstein output error systems using Levenberg–Marquardt optimization method with varying interval measurements☆
Journal of the Franklin Institute, 2017Co-Authors: Li Junhong, Wei Xing Zheng, Liang HuaAbstract:This paper studies the parameter estimation problem of Hammerstein output error autoregressive (OEAR) systems. According to the maximum likelihood principle and the Levenberg–Marquardt optimization method, a maximum likelihood Levenberg–Marquardt recursive (ML-LM-R) algorithm using the varying interval input–output data is proposed. Furthermore, a stochastic gradient algorithm is also derived in order to compare it with the proposed ML-LM-R algorithm. Two numerical examples are provided to verify the effectiveness of the proposed algorithms.
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Parameter identification for Hammerstein nonlinear systems using the maximum likelihood principle and Levenberg-Marquardt optimization method
2016 35th Chinese Control Conference (CCC), 2016Co-Authors: Li Junhong, Wei Xing Zheng, Liang HuaAbstract:This paper studies the parameter estimation problems of Hammerstein output error autoregressive (OEAR) systems. A maximum likelihood Levenberg-Marquardt recursive (ML-LM-R) algorithm using the varying interval input-output data is presented by using the maximum likelihood principle and Levenberg-Marquardt optimization method. The effectiveness of the algorithm is verified by a numerical example.
Bogdan M. Wilamowski - One of the best experts on this subject based on the ideXlab platform.
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improved computation for Levenberg Marquardt training
IEEE Transactions on Neural Networks, 2010Co-Authors: Bogdan M. WilamowskiAbstract:The improved computation presented in this paper is aimed to optimize the neural networks learning process using Levenberg-Marquardt (LM) algorithm. Quasi-Hessian matrix and gradient vector are computed directly, without Jacobian matrix multiplication and storage. The memory limitation problem for LM training is solved. Considering the symmetry of quasi-Hessian matrix, only elements in its upper/lower triangular array need to be calculated. Therefore, training speed is improved significantly, not only because of the smaller array stored in memory, but also the reduced operations in quasi-Hessian matrix calculation. The improved memory and time efficiencies are especially true for large sized patterns training.
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Improved Computation for Levenberg–Marquardt Training
IEEE transactions on neural networks, 2010Co-Authors: Bogdan M. WilamowskiAbstract:The improved computation presented in this paper is aimed to optimize the neural networks learning process using Levenberg-Marquardt (LM) algorithm. Quasi-Hessian matrix and gradient vector are computed directly, without Jacobian matrix multiplication and storage. The memory limitation problem for LM training is solved. Considering the symmetry of quasi-Hessian matrix, only elements in its upper/lower triangular array need to be calculated. Therefore, training speed is improved significantly, not only because of the smaller array stored in memory, but also the reduced operations in quasi-Hessian matrix calculation. The improved memory and time efficiencies are especially true for large sized patterns training.
Jonas Sjöberg - One of the best experts on this subject based on the ideXlab platform.
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Efficient training of neural nets for nonlinear adaptive filtering using a recursive Levenberg-Marquardt algorithm
IEEE Transactions on Signal Processing, 2000Co-Authors: Lester S.h. Ngia, Jonas SjöbergAbstract:The Levenberg-Marquardt algorithm is often superior to other training algorithms in off-line applications. This motivates the proposal of using a recursive version of the algorithm for on-line training of neural nets for nonlinear adaptive filtering. The performance of the suggested algorithm is compared with other alternative recursive algorithms, such as the recursive version of the off-line steepest-descent and Gauss-Newton algorithms. The advantages and disadvantages of the different algorithms are pointed out. The algorithms are tested on some examples, and it is shown that generally the recursive Levenberg-Marquardt algorithm has better convergence properties than the other algorithms.