Masked Multiple State Space Model Identification Using FRD and Evolutionary Optimization

Identification of dynamical systems from frequency response data (FRD) has extensively been studied and effective techniques have been developed. Given different FRD sets obtained from different systems and a fixed state space model structure, is it possible to find a constant parameter vector conta...

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Vydané v:IEEE transactions on industrial informatics Ročník 20; číslo 7; s. 9861 - 9869
Hlavní autori: Efe, Mehmet Onder, Kurkcu, Burak, Kasnakoglu, Cosku, Mohamed, Zaharuddin, Liu, Zhijie
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.07.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Costs
Dynamic models
Dynamical systems
Frequency response
Genetic algorithms
Genetic algorithms (GAs)
identification
masked models
Optimization
Parameters
Social factors
State space models
State-space methods
System effectiveness
Time-frequency analysis
ISSN:1551-3203, 1941-0050
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Shrnutí:Identification of dynamical systems from frequency response data (FRD) has extensively been studied and effective techniques have been developed. Given different FRD sets obtained from different systems and a fixed state space model structure, is it possible to find a constant parameter vector containing <inline-formula><tex-math notation="LaTeX">(\mathbf {A},\mathbf {B},\mathbf {C},\mathbf {D})</tex-math></inline-formula> quadruple's numerical content and a FRD-associated mask vector set that approximates the spectral information available in each FRD set? This article proposes a genetic algorithm based optimization approach to determine the real parameter vector <inline-formula><tex-math notation="LaTeX">(\mathbf {A},\mathbf {B},\mathbf {C},\mathbf {D})</tex-math></inline-formula> and the binary mask vector through a sequential optimization scheme. We study state space models for matching FRD from multiple systems. Results show that the proposed optimization approach solves the problem and compresses multiple dynamical models into a single masked one.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2024.3388605