A novel optimization algorithm for MIMO Hammerstein model identification under heavy-tailed noise

doi:10.1016/j.isatra.2017.10.001

	A novel optimization algorithm for MIMO Hammerstein model identification under heavy-tailed noise
	Jin, Qibing 2; Wang, Hehe 2; Su, Qixin 2; Jiang, Beiyan 2; Liu, Qie 1
刊名	ISA TRANSACTIONS
	2018
卷号	72 页码:77-91
关键词	Cuckoo Search Gmda Mimo Hammerstein Model Heavy-tailed Noise Radial Basis Function Neural Network
ISSN号	0019-0578
DOI	10.1016/j.isatra.2017.10.001
文献子类	Article
英文摘要	In this paper, we study the system identification of multi-input multi-output (MIMO) Hammerstein processes under the typical heavy-tailed noise. To the best of our knowledge, there is no general analytical method to solve this identification problem. Motivated by this, we propose a general identification method to solve this problem based on a Gaussian-Mixture Distribution intelligent optimization algorithm (GMDA). The nonlinear part of Hammerstein process is modeled by a Radial Basis Function (RBF) neural network, and the identification problem is converted to an optimization problem. To overcome the drawbacks of analytical identification method in the presence of heavy-tailed noise, a meta-heuristic optimization algorithm, Cuckoo search (CS) algorithm is used. To improve its performance for this identification problem, the Gaussian-mixture Distribution (GMD) and the GMD sequences are introduced to improve the performance of the standard CS algorithm. Numerical simulations for different MIMO Hammerstein models are carried out, and the simulation results verify the effectiveness of the proposed GMDA. (C) 2017 ISA. Published by Elsevier Ltd. All rights reserved.
WOS关键词	BASIS-FUNCTION NETWORKS ; SYSTEM-IDENTIFICATION ; ROBUST ESTIMATION ; T-DISTRIBUTION ; APPROXIMATION ; CHANNELS
WOS研究方向	Automation & Control Systems ; Engineering ; Instruments & Instrumentation
语种	英语
出版者	ELSEVIER SCIENCE INC
WOS记录号	WOS:000424960100009
资助机构	National Natural Science Foundation of China(61673004 ; 61273132)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/28234]
专题	中国科学院自动化研究所
通讯作者	Wang, Hehe
作者单位	1.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China 2.Beijing Univ Chem Technol, Inst Automat, Beisanhuan East Rd 15, Beijing 100029, Peoples R China
推荐引用方式 GB/T 7714	Jin, Qibing,Wang, Hehe,Su, Qixin,et al. A novel optimization algorithm for MIMO Hammerstein model identification under heavy-tailed noise[J]. ISA TRANSACTIONS,2018,72:77-91.
APA	Jin, Qibing,Wang, Hehe,Su, Qixin,Jiang, Beiyan,&Liu, Qie.(2018).A novel optimization algorithm for MIMO Hammerstein model identification under heavy-tailed noise.ISA TRANSACTIONS,72,77-91.
MLA	Jin, Qibing,et al."A novel optimization algorithm for MIMO Hammerstein model identification under heavy-tailed noise".ISA TRANSACTIONS 72(2018):77-91.