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

Ammara Mehmood1, Naveed Ishtiaq Chaudhary2, Aneela Zameer3

  • 1Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad, Pakistan.

ISA Transactions
|February 17, 2019
PubMed
Summary

This study introduces evolutionary algorithms like BSA, DE, and GAs for identifying parameters in nonlinear Hammerstein systems. The Backtracking Search Algorithm (BSA) demonstrated superior performance in optimizing the system

Keywords:
Backtracking search optimizationDifferential evolutionEvolutionary computationsGenetic algorithmsHammerstein systemsParameter estimation

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Area of Science:

  • Control Systems Engineering
  • Computational Intelligence
  • System Identification

Background:

  • Nonlinear Hammerstein systems present complex parameter identification challenges.
  • Evolutionary computational heuristics offer robust global search capabilities.

Purpose of the Study:

  • To apply and compare evolutionary algorithms for parameter identification in nonlinear Hammerstein Auto Regressive Auto Regressive (NHCARAR) systems.
  • To evaluate the performance of Backtracking Search Algorithm (BSA), Differential Evolution (DE), and Genetic Algorithms (GAs).

Main Methods:

  • Utilizing mean squared error as the fitness function for NHCARAR system parameter estimation.
  • Optimizing the cost function using BSA with variations in degrees of freedom and noise variances.
  • Comparative analysis with DE and GAs using statistical metrics.

Main Results:

  • BSA showed effective optimization for the NHCARAR model.
  • Statistical observations validated the performance of the proposed scheme.
  • Comparative studies highlighted the efficiency of BSA over DE and GAs.

Conclusions:

  • Evolutionary algorithms, particularly BSA, are effective for parameter identification in NHCARAR systems.
  • The study provides a validated approach for complex system modeling.
  • BSA offers a competitive alternative for parameter estimation in nonlinear systems.