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ISA Trans. 2019 Feb 6. pii: S0019-0578(19)30067-9. doi: 10.1016/j.isatra.2019.01.042. [Epub ahead of print]

Backtracking search optimization heuristics for nonlinear Hammerstein controlled auto regressive auto regressive systems.

Author information

1
Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad, Pakistan. Electronic address: ms_mehmud16@pieas.edu.pk.
2
Department of Electrical Engineering, International Islamic University, Islamabad, Pakistan. Electronic address: naveed.ishtiaq@iiu.edu.pk.
3
Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad, Pakistan. Electronic address: aneelaz@pieas.edu.pk.
4
Department of Electrical and Computer Engineering, COMSATS University Islamabad, Attock Campus, Attock, Pakistan. Electronic address: Muhammad.asif@ciit-attock.edu.pk.

Abstract

In this work, novel application of evolutionary computational heuristics is presented for parameter identification problem of nonlinear Hammerstein controlled auto regressive auto regressive (NHCARAR) systems through global search competency of backtracking search algorithm (BSA), differential evolution (DE) and genetic algorithms (GAs). The mean squared error metric is used for the fitness function of NHCARAR system based on difference between actual and approximated design variables. Optimization of the cost function is conducted with BSA for NHCARAR model by varying degrees of freedom and noise variances. To verify and validate the worth of the presented scheme, comparative studies are carried out with its counterparts DE and GAs through statistical observations by means of weight deviation factor, root of mean squared error, and Thiel's inequality coefficient as well as complexity measures.

KEYWORDS:

Backtracking search optimization; Differential evolution; Evolutionary computations; Genetic algorithms; Hammerstein systems; Parameter estimation

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