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Backtracking search optimization heuristics for nonlinear Hammerstein controlled auto regressive auto regressive systems
- Source :
- ISA Transactions. 91:99-113
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
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.
- Subjects :
- 0209 industrial biotechnology
Fitness function
Mean squared error
Computer science
Estimation theory
Backtracking
Applied Mathematics
020208 electrical & electronic engineering
02 engineering and technology
Computer Science Applications
Parameter identification problem
020901 industrial engineering & automation
Autoregressive model
Control and Systems Engineering
Differential evolution
0202 electrical engineering, electronic engineering, information engineering
Electrical and Electronic Engineering
Heuristics
Instrumentation
Algorithm
Subjects
Details
- ISSN :
- 00190578
- Volume :
- 91
- Database :
- OpenAIRE
- Journal :
- ISA Transactions
- Accession number :
- edsair.doi.dedup.....fe94fee9da112d3d943e954eb799cca4