Fuzzy state space modeling for solving inverse problems in multivariable dynamic systems

The main objective of this study is to develop a novel inverse modeling technique, known as Fuzzy State Space Model (FSSM). This model is used for optimization of input parameters in multivariable dynamic systems. In this approach, the flexibility of fuzzy modeling is incorporated with the crisp sta...

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Main Author:	Ismail, Razidah
Format:	Thesis
Language:	English
Published:	2005
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/4298/1/RazidahIsmailPFS2005.pdf
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id	my-utm-ep.4298
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spelling	my-utm-ep.42982018-01-16T08:32:42Z Fuzzy state space modeling for solving inverse problems in multivariable dynamic systems 2005-05 Ismail, Razidah QA75 Electronic computers. Computer science The main objective of this study is to develop a novel inverse modeling technique, known as Fuzzy State Space Model (FSSM). This model is used for optimization of input parameters in multivariable dynamic systems. In this approach, the flexibility of fuzzy modeling is incorporated with the crisp state space models proposed in the modern control theory. The vagueness and uncertainty of the parameters are represented in the model construction, as a way of increasing the available information in order to achieve a more precise model of reality. Subsequently, the inverse Fuzzy State Space algorithm is formulated for a multipleinput single-output system, which leads to the derivation of Modified Optimized Defuzzified Value Theorem. This algorithm is enhanced to address the optimization of parameters for a multiple-input multiple-output system, which leads to the derivation of an Extension of Optimized Defuzzified Value Theorem. The proofs of these theorems are presented. To facilitate the implementation of these algorithms, a semi-automated computational tool using MatlabÂ® programming facilities is developed. The effectiveness of this modeling approach is illustrated by implementing it to the state space model of a furnace system of a combined cycle power plant. The results obtained in this application demonstrate that the proposed new modeling approach is reasonable and provides an innovative tool for decision-makers. In addition, the investigations on the properties and characteristics of FSSM have resulted in the derivation of some lemma and theorems related to convexity and normality of the induced solution of the model, and bounded stability of the Fuzzy State Space system. Finally, the properties of the induced solution of a single FSSM are generalized to the multi-connected systems of FSSM. Some algebraic views related to the systems of FSSM are also discussed. 2005-05 Thesis http://eprints.utm.my/id/eprint/4298/ http://eprints.utm.my/id/eprint/4298/1/RazidahIsmailPFS2005.pdf application/pdf en public phd doctoral Universiti Teknologi Malaysia, Faculty of Science Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Ismail, Razidah Fuzzy state space modeling for solving inverse problems in multivariable dynamic systems
description	The main objective of this study is to develop a novel inverse modeling technique, known as Fuzzy State Space Model (FSSM). This model is used for optimization of input parameters in multivariable dynamic systems. In this approach, the flexibility of fuzzy modeling is incorporated with the crisp state space models proposed in the modern control theory. The vagueness and uncertainty of the parameters are represented in the model construction, as a way of increasing the available information in order to achieve a more precise model of reality. Subsequently, the inverse Fuzzy State Space algorithm is formulated for a multipleinput single-output system, which leads to the derivation of Modified Optimized Defuzzified Value Theorem. This algorithm is enhanced to address the optimization of parameters for a multiple-input multiple-output system, which leads to the derivation of an Extension of Optimized Defuzzified Value Theorem. The proofs of these theorems are presented. To facilitate the implementation of these algorithms, a semi-automated computational tool using MatlabÂ® programming facilities is developed. The effectiveness of this modeling approach is illustrated by implementing it to the state space model of a furnace system of a combined cycle power plant. The results obtained in this application demonstrate that the proposed new modeling approach is reasonable and provides an innovative tool for decision-makers. In addition, the investigations on the properties and characteristics of FSSM have resulted in the derivation of some lemma and theorems related to convexity and normality of the induced solution of the model, and bounded stability of the Fuzzy State Space system. Finally, the properties of the induced solution of a single FSSM are generalized to the multi-connected systems of FSSM. Some algebraic views related to the systems of FSSM are also discussed.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Ismail, Razidah
author_facet	Ismail, Razidah
author_sort	Ismail, Razidah
title	Fuzzy state space modeling for solving inverse problems in multivariable dynamic systems
title_short	Fuzzy state space modeling for solving inverse problems in multivariable dynamic systems
title_full	Fuzzy state space modeling for solving inverse problems in multivariable dynamic systems
title_fullStr	Fuzzy state space modeling for solving inverse problems in multivariable dynamic systems
title_full_unstemmed	Fuzzy state space modeling for solving inverse problems in multivariable dynamic systems
title_sort	fuzzy state space modeling for solving inverse problems in multivariable dynamic systems
granting_institution	Universiti Teknologi Malaysia, Faculty of Science
granting_department	Faculty of Science
publishDate	2005
url	http://eprints.utm.my/id/eprint/4298/1/RazidahIsmailPFS2005.pdf
_version_	1747814509875036160

Fuzzy state space modeling for solving inverse problems in multivariable dynamic systems

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