A novel single parent mating technique in genetic algorithm for discrete - time system identification

System identification is concerned with the construction of a mathematical model based on given input and output data to represent the dynamical behaviour of a system. As a step in system identification, model structure selection is a step where a model perceived as adequate system representation is...

Full description

Saved in:
Bibliographic Details
Main Authors: Abd Samad @ Mahmood, Md Fahmi, Zainuddin, Farah Ayiesya, Jamaluddin, Hishamuddin, Azad, Abul K. M.
Format: Article
Language:English
Published: Semarak Ilmu Publishing 2024
Online Access:http://eprints.utem.edu.my/id/eprint/27526/2/003120306202411425840.PDF
http://eprints.utem.edu.my/id/eprint/27526/
https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/4185/4377
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Universiti Teknikal Malaysia Melaka
Language: English
Description
Summary:System identification is concerned with the construction of a mathematical model based on given input and output data to represent the dynamical behaviour of a system. As a step in system identification, model structure selection is a step where a model perceived as adequate system representation is selected. A typical rule is that the model must have a good balance between parsimony and accuracy in estimating a dynamic system. As a popular search method, genetic algorithm (GA) is used for selecting a model structure. However, the optimality of the final model depends much on the optimality of GA. This paper introduces a novel mating technique in GA based on the chromosome structure of the parents such that a single parent is sufficient in achieving mating that demonstrates high exploration capability. In investigating this, four systems of linear and nonlinear classes were simulated to generate discrete-time sets of data i.e. later used for identification. The outcome shows that GA incorporated with the mating technique within 10%-20% of the population size is able to find optimal models quicker than the traditional GA.