Artificial Ecosystem Optimizer-Based System Identification and Its Performance Evaluation

This study delves into the realm of system identification, a crucial sub-field in control engineering, aimed at constructing mathematical models of systems based on input/output data. This work particularly proposes the application of artificial ecosystem algorithm (AEO) for solving system identific...

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Published inArabian journal for science and engineering (2011) Vol. 49; no. 12; pp. 16083 - 16106
Main Author Fidan, Şehmus
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024
Springer Nature B.V
Subjects
Algorithms
Energy flow
Engineering
Heuristic methods
Humanities and Social Sciences
multidisciplinary
Parameter identification
Performance evaluation
Research Article-Electrical Engineering
Science
System identification
Transfer functions
Meta-heuristic algorithm
Air stream heater process
System identification
Artificial ecosystem algorithm
Online AccessGet full text
ISSN2193-567X
1319-8025
2191-4281
2191-4281
DOI10.1007/s13369-024-08841-w

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Abstract This study delves into the realm of system identification, a crucial sub-field in control engineering, aimed at constructing mathematical models of systems based on input/output data. This work particularly proposes the application of artificial ecosystem algorithm (AEO) for solving system identification problems. Inspired by the energy flow of natural ecosystems, AEO has undergone specific modifications leading to derived versions. Additionally, five diverse meta-heuristic algorithms are employed to assess their applicability and performance in system identification using data from an air stream heater experiment kit. A comprehensive performance comparison is made, considering time bounds, maximum generations, early stopping, and function evaluation constraints, presenting their respective performances. Among the evaluated algorithms, the AEO algorithm enhanced with the sine and cosine strategy stands out with a determined R 2 value of 0.951. This algorithm consistently outperforms others in Wilcoxon tests, showcasing its significant success. Our study affirms that meta-heuristic algorithms, particularly the proposed AEO algorithm, can be effectively applied to system identification problems, yielding successful calculations of transfer function parameters.
AbstractList This study delves into the realm of system identification, a crucial sub-field in control engineering, aimed at constructing mathematical models of systems based on input/output data. This work particularly proposes the application of artificial ecosystem algorithm (AEO) for solving system identification problems. Inspired by the energy flow of natural ecosystems, AEO has undergone specific modifications leading to derived versions. Additionally, five diverse meta-heuristic algorithms are employed to assess their applicability and performance in system identification using data from an air stream heater experiment kit. A comprehensive performance comparison is made, considering time bounds, maximum generations, early stopping, and function evaluation constraints, presenting their respective performances. Among the evaluated algorithms, the AEO algorithm enhanced with the sine and cosine strategy stands out with a determined R 2 value of 0.951. This algorithm consistently outperforms others in Wilcoxon tests, showcasing its significant success. Our study affirms that meta-heuristic algorithms, particularly the proposed AEO algorithm, can be effectively applied to system identification problems, yielding successful calculations of transfer function parameters.
This study delves into the realm of system identification, a crucial sub-field in control engineering, aimed at constructing mathematical models of systems based on input/output data. This work particularly proposes the application of artificial ecosystem algorithm (AEO) for solving system identification problems. Inspired by the energy flow of natural ecosystems, AEO has undergone specific modifications leading to derived versions. Additionally, five diverse meta-heuristic algorithms are employed to assess their applicability and performance in system identification using data from an air stream heater experiment kit. A comprehensive performance comparison is made, considering time bounds, maximum generations, early stopping, and function evaluation constraints, presenting their respective performances. Among the evaluated algorithms, the AEO algorithm enhanced with the sine and cosine strategy stands out with a determined R2 value of 0.951. This algorithm consistently outperforms others in Wilcoxon tests, showcasing its significant success. Our study affirms that meta-heuristic algorithms, particularly the proposed AEO algorithm, can be effectively applied to system identification problems, yielding successful calculations of transfer function parameters.
Author Fidan, Şehmus
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  organization: Vocational School of Technical Sciences, Electronic Program, Batman University
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Keywords Meta-heuristic algorithm
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Artificial ecosystem algorithm
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Snippet This study delves into the realm of system identification, a crucial sub-field in control engineering, aimed at constructing mathematical models of systems...
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SubjectTerms Algorithms
Energy flow
Engineering
Heuristic methods
Humanities and Social Sciences
multidisciplinary
Parameter identification
Performance evaluation
Research Article-Electrical Engineering
Science
System identification
Transfer functions
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Title Artificial Ecosystem Optimizer-Based System Identification and Its Performance Evaluation
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