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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Abstract	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.
AbstractList	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 [Formula Omitted] 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 [Formula Omitted] 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. 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.
Author	Liu, Zhijie Kurkcu, Burak Efe, Mehmet Onder Kasnakoglu, Cosku Mohamed, Zaharuddin
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SubjectTerms	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
Title	Masked Multiple State Space Model Identification Using FRD and Evolutionary Optimization
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