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Neural Network Approach to Solving Fully Fuzzy Nonlinear Systems
Departamento de Control Automático, CINVESTAV-IPN (National Polytechnic Institute), Mexico City, Mexico.
Department of Information and Communication Technology, Agder University College, Grimstad, Norway.
School of Computing, University of Portsmouth, Portsmouth, United States.
Departamento de Control Automático, CINVESTAV-IPN (National Polytechnic Institute), Mexico City, Mexico.
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2018 (English)In: Fuzzy Modeling and Control: Methods, Applications and Research / [ed] Terrell Harvey & Dallas Mullins, Nova Science Publishers, Inc., 2018, p. 46-68Chapter in book (Refereed)
Abstract [en]

The value of fuzzy designs improves whenever a system cannot be validated in precise mathematical terminologies. In this book chapter, two types of neural networks are applied to obtain the approximate solutions of the fully fuzzy nonlinear system (FFNS). For obtaining the approximate solutions, a superior gradient descent algorithmis proposed in order to train the neural networks. Several examples are illustrated to disclosehigh precision as well as the effectiveness of the proposed methods. The MATLAB environment is utilized to generate the simulations.

Place, publisher, year, edition, pages
Nova Science Publishers, Inc., 2018. p. 46-68
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:oru:diva-71792Scopus ID: 2-s2.0-85058558000ISBN: 978-1-53613-414-8 (print)OAI: oai:DiVA.org:oru-71792DiVA, id: diva2:1281995
Available from: 2019-01-23 Created: 2019-01-23 Last updated: 2019-01-25Bibliographically approved

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Paul, Satyam

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CiteExportLink to record
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Citation style
  • apa
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  • Other locale
More languages
Output format
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  • asciidoc
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