Model Identification Using Neuro-Fuzzy Approach

This chapter contains the discussion on fundamental concepts related to nonlinear model identification. First, linear in parameter model identification techniques are presented. This covers static and dynamic systems. Following that, the idea of developing nonlinear models in the framework of Orhono...

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Main Author: Lemma, T.A.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.21263 /
Published: Springer Verlag 2018
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039982132&doi=10.1007%2f978-3-319-71871-2_3&partnerID=40&md5=5b2e4f17de24ac277fb456d437b527ce
http://eprints.utp.edu.my/21263/
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spelling utp-eprints.212632019-02-26T03:18:54Z Model Identification Using Neuro-Fuzzy Approach Lemma, T.A. This chapter contains the discussion on fundamental concepts related to nonlinear model identification. First, linear in parameter model identification techniques are presented. This covers static and dynamic systems. Following that, the idea of developing nonlinear models in the framework of Orhonormal Basis Functions (OBF) is described. In Sect. 3.3, basic theory of neural networks and fuzzy systems are elaborated. In the state of the art designs, one of them is constructed in the structure of the other allowing the development of a transparent model that can be trained with relatively minimal effort. Section 3.4 is dedicated to the discussion of nonlinear system identification using combined version of neural networks and fuzzy systems. Last section of the chapter deals with three different model training algorithms Least squares based, back-propagation and particle swarm optimization. © 2018, Springer International Publishing AG. Springer Verlag 2018 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039982132&doi=10.1007%2f978-3-319-71871-2_3&partnerID=40&md5=5b2e4f17de24ac277fb456d437b527ce Lemma, T.A. (2018) Model Identification Using Neuro-Fuzzy Approach. Studies in Computational Intelligence, 743 . pp. 37-74. http://eprints.utp.edu.my/21263/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description This chapter contains the discussion on fundamental concepts related to nonlinear model identification. First, linear in parameter model identification techniques are presented. This covers static and dynamic systems. Following that, the idea of developing nonlinear models in the framework of Orhonormal Basis Functions (OBF) is described. In Sect. 3.3, basic theory of neural networks and fuzzy systems are elaborated. In the state of the art designs, one of them is constructed in the structure of the other allowing the development of a transparent model that can be trained with relatively minimal effort. Section 3.4 is dedicated to the discussion of nonlinear system identification using combined version of neural networks and fuzzy systems. Last section of the chapter deals with three different model training algorithms Least squares based, back-propagation and particle swarm optimization. © 2018, Springer International Publishing AG.
format Article
author Lemma, T.A.
spellingShingle Lemma, T.A.
Model Identification Using Neuro-Fuzzy Approach
author_sort Lemma, T.A.
title Model Identification Using Neuro-Fuzzy Approach
title_short Model Identification Using Neuro-Fuzzy Approach
title_full Model Identification Using Neuro-Fuzzy Approach
title_fullStr Model Identification Using Neuro-Fuzzy Approach
title_full_unstemmed Model Identification Using Neuro-Fuzzy Approach
title_sort model identification using neuro-fuzzy approach
publisher Springer Verlag
publishDate 2018
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039982132&doi=10.1007%2f978-3-319-71871-2_3&partnerID=40&md5=5b2e4f17de24ac277fb456d437b527ce
http://eprints.utp.edu.my/21263/
_version_ 1741196431527510016
score 11.62408

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