Identification of Hammerstein Model using Bacteria Foraging Optimization Algorithm

被引：0

作者：

Pal, P. S. ^{[1
]}

Ghosh, A. ^{[1
]}

Choudhury, S. ^{[1
]}

Debapriya, D.

Kar, R. ^{[1
]}

Mandal, D. ^{[1
]}

Ghoshal, S. P. ^{[2
]}

机构：

[1] NIT Durgapur, Dept ECE, Durgapur, India

[2] Natl Inst Technol, Dept Elect Engn, Durgapur, India

来源：

2016 INTERNATIONAL CONFERENCE ON COMMUNICATION AND SIGNAL PROCESSING (ICCSP), VOL. 1 | 2016年

关键词：

BFO; Convergence; MSE; NARMAX Hammerstein model; Parametric identification; SYSTEMS; NONLINEARITIES;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper presents an efficient approach for identification of a nonlinear Hammerstein model using Bacteria Foraging Optimization (BFO) Algorithm. The accuracy and the efficiency of the proposed BFO based identification scheme have been justified with the optimal value of MSE and the corresponding comparative statistical information. The statistical information of the MSE has also been provided to justify consistency of the BFO algorithm for identification of the Hammerstein model. The estimated parameters along with their corresponding deviations and convergences are shown to justify efficiency of the proposed identification strategy. The deviations of the estimated parameters from their actual values are also reported to justify precision and effectiveness of the BFO based identification approach.

引用

页码：1609 / 1613

页数：5

共 50 条

[41] Parameter Estimation of Hammerstein Model Based on a Recursive Algorithm in Wavelet Domain [J].

Li Zhen-Qiang ;

Ye Hong-Tao ;

Luo Wen-Guang .

2013 32ND CHINESE CONTROL CONFERENCE (CCC), 2013, :1670-1675

[42] Identification Approach of the Hammerstein-Wiener Model Applying Combined Signals [J].

Zhou, Shibo ;

Ding, Zhenyu ;

Li, Feng .

2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS, 2023, :938-942

[43] A Novel Meta-Heuristic Algorithm for Numerical and Engineering Optimization Problems: Piranha Foraging Optimization Algorithm (PFOA) [J].

Cao, Shuai ;

Qian, Qian ;

Cao, Yongjun ;

Li, Wenwei ;

Huang, Weixi ;

Liang, Jianan .

IEEE ACCESS, 2023, 11 :92505-92522

[44] Iterative and recursive subspace algorithms for identification of Hammerstein model with SH nonlinearity [J].

Aissaoui, Borhen ;

Soltani, Moez ;

Chaari, Abdelkader .

2017 INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND DIAGNOSIS (ICCAD), 2017, :355-360

[45] Identification of a precision motion stage based on the Hammerstein-Wiener model [J].

Zhang, Zhu ;

Zhang, Delong ;

Zheng, Haiyang ;

Huang, Tao ;

Xie, Yangqiu .

PROCEEDINGS OF THE 38TH CHINESE CONTROL CONFERENCE (CCC), 2019, :1637-1642

[46] Identification of Wiener-Hammerstein benchmark model via rank minimization [J].

Han, Younghee ;

de Callafon, Raymond A. .

CONTROL ENGINEERING PRACTICE, 2012, 20 (11) :1149-1155

[47] Bias-Correction Errors-in-Variables Hammerstein Model Identification [J].

Hou, Jie ;

Su, Hao ;

Yu, Chengpu ;

Chen, Fengwei ;

Li, Penghua .

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2023, 70 (07) :7268-7279

[48] A subspace-based identification of Wiener-Hammerstein benchmark model [J].

Ase, Hajime ;

Katayama, Tohru .

CONTROL ENGINEERING PRACTICE, 2015, 44 :126-137

[49] Frequency domain global optimization algorithm for the aircraft flutter model parameter identification [J].

Wang, Jianhong ;

Wang, Daobo ;

Wang, Zhisheng .

2010 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-5, 2010, :3340-3344

[50] Improved identification of Hammerstein plants using new CPSO and IPSO algorithms [J].

Nanda, Satyasai Jagannath ;

Panda, Ganapati ;

Majhi, Babita .

EXPERT SYSTEMS WITH APPLICATIONS, 2010, 37 (10) :6818-6831

← 1 2 3 4 5 →