Decomposition- and Gradient-Based Iterative Identification Algorithms for Multivariable Systems Using the Multi-innovation Theory

Circuits Systems and Signal Processing ◽

10.1007/s00034-018-1014-2 ◽

2019 ◽

Vol 38 (7) ◽

pp. 2971-2991 ◽

Author(s):

Lijuan Wan ◽

Feng Ding

Keyword(s):

Multivariable Systems ◽

Innovation Theory ◽

Iterative Identification ◽

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Partially coupled gradient-based iterative identification methods for multivariable output-error moving average systems

International Journal of Modelling Identification and Control ◽

10.1504/ijmic.2016.081139 ◽

2016 ◽

Vol 26 (4) ◽

pp. 293 ◽

Author(s):

Feifei Wang ◽

Feng Ding

Keyword(s):

Moving Average ◽

Iterative Identification ◽

Identification Methods ◽

Output Error ◽

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Iterative identification methods for input nonlinear multivariable systems using the key-term separation principle

Journal of the Franklin Institute ◽

10.1016/j.jfranklin.2015.05.005 ◽

2015 ◽

Vol 352 (7) ◽

pp. 2847-2865 ◽

Author(s):

Qianyan Shen ◽

Feng Ding

Keyword(s):

Multivariable Systems ◽

Separation Principle ◽

Iterative Identification ◽

Identification Methods

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Hierarchical least-squares based iterative identification for multivariable systems with moving average noises

Mathematical and Computer Modelling ◽

10.1016/j.mcm.2010.01.003 ◽

2010 ◽

Vol 51 (9-10) ◽

pp. 1213-1220 ◽

Author(s):

Heqiang Han ◽

Li Xie ◽

Feng Ding ◽

Xinggao Liu

Keyword(s):

Least Squares ◽

Moving Average ◽

Multivariable Systems ◽

Iterative Identification

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Gradient-Based Iterative Identification for Wiener Nonlinear Dynamic Systems with Moving Average Noises

10.3390/a8030712 ◽

2015 ◽

Vol 8 (3) ◽

pp. 712-722 ◽

Author(s):

Lincheng Zhou ◽

Xiangli Li ◽

Huigang Xu ◽

Peiyi Zhu

Keyword(s):

Dynamic Systems ◽

Nonlinear Dynamic ◽

Moving Average ◽

Nonlinear Dynamic Systems ◽

Iterative Identification ◽

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Gradient-based iterative identification method for multivariate equation-error autoregressive moving average systems using the decomposition technique

Journal of the Franklin Institute ◽

10.1016/j.jfranklin.2018.12.002 ◽

2019 ◽

Vol 356 (3) ◽

pp. 1658-1676 ◽

Author(s):

Zhengwei Ge ◽

Feng Ding ◽

Ling Xu ◽

Ahmed Alsaedi ◽

Tasawar Hayat

Keyword(s):

Moving Average ◽

Autoregressive Moving Average ◽

Decomposition Technique ◽

Iterative Identification ◽

Identification Method ◽

Equation Error ◽

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Gradient-based iterative identification for Wiener nonlinear systems with non-uniform sampling

Nonlinear Dynamics ◽

10.1007/s11071-013-1156-5 ◽

2013 ◽

Vol 76 (1) ◽

pp. 627-634 ◽

Author(s):

Lincheng Zhou ◽

Xiangli Li ◽

Feng Pan

Keyword(s):

Nonlinear Systems ◽

Iterative Identification ◽

Uniform Sampling ◽

Gradient Based ◽

Non Uniform Sampling

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Least squares based and gradient based iterative identification for Wiener nonlinear systems

Signal Processing ◽

10.1016/j.sigpro.2010.11.004 ◽

2011 ◽

Vol 91 (5) ◽

pp. 1182-1189 ◽

Author(s):

Dongqing Wang ◽

Feng Ding

Keyword(s):

Nonlinear Systems ◽

Least Squares ◽

Iterative Identification ◽

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Gradient-based and multi-innovation gradient-based iterative identification methods for dual-diode photovoltaic cell models

10.1109/ccdc52312.2021.9602842 ◽

2021 ◽

Author(s):

Xiangxiang Meng ◽

Yan Ji

Keyword(s):

Photovoltaic Cell ◽

Cell Models ◽

Iterative Identification ◽

Identification Methods ◽

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Partially coupled gradient-based iterative identification methods for multivariable output-error moving average systems

International Journal of Modelling Identification and Control ◽

10.1504/ijmic.2016.10002141 ◽

2016 ◽

Vol 26 (4) ◽

pp. 293

Author(s):

Feng Ding ◽

Feifei Wang

Keyword(s):

Moving Average ◽

Iterative Identification ◽

Identification Methods ◽

Output Error ◽

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Gradient-based iterative identification methods for multivariate pseudo-linear moving average systems using the data filtering

Nonlinear Dynamics ◽

10.1007/s11071-016-2623-6 ◽

2016 ◽

Vol 84 (4) ◽

pp. 2003-2015 ◽

Author(s):

Feifei Wang ◽

Feng Ding

Keyword(s):

Moving Average ◽

Data Filtering ◽

Iterative Identification ◽

Identification Methods ◽

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