Bio-inspired computational heuristics for parameter estimation of nonlinear Hammerstein controlled autoregressive system

被引：59

作者：

Raja, Muhammad Asif Zahoor ^{[1
]}

Shah, Abbas Ali ^{[1
]}

Mehmood, Ammara ^{[1
]}

Chaudhary, Naveed Ishtiaq ^{[2
]}

Aslam, Muhammad Saeed ^{[3
]}

机构：

[1] COMSATS Inst Informat Technol, Dept Elect Engn, Attock, Pakistan

[2] Int Islamic Univ, Dept Elect Engn, Islamabad, Pakistan

[3] Pakistan Inst Engn & Appl Sci, Islamabad, Pakistan

来源：

NEURAL COMPUTING & APPLICATIONS | 2018年 / 29卷 / 12期

关键词：

System identification; Evolutionary computing; Hammerstein systems; Computational heuristics; Genetic algorithms; NEURAL-NETWORK MODEL; LEAST-SQUARES IDENTIFICATION; NUMERICAL TREATMENT; GENETIC ALGORITHM; SWARM OPTIMIZATION; DESIGN;

D O I：

10.1007/s00521-016-2677-x

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this study, strength of evolutionary computational intelligence based on genetic algorithms (GAs) is exploited for parameter identification of nonlinear Hammerstein controlled autoregressive (NHCAR) systems. The fitness function is constructed for the NHCAR system by defining an error function in the mean square sense. Unknown adjustable weights of the system are optimized with GAs, used as an effective tool for effective global search. Comparative analysis of the proposed scheme is made from true parameters of the systems for a number of scenarios based on different levels of signal-to-noise ratios. The validation of the performance is made through statistics based on sufficiently large number of runs using indices of mean absolute error, variance account for, and Thiel's inequality coefficient as well as their global versions.

引用

页码：1455 / 1474

页数：20

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