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Constrained optimization of genetic fuzzy systems

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Accuracy Improvements in Linguistic Fuzzy Modeling

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 129))

Abstract

Although fuzzy systems and artificial neural networks are both universal function approximators, fuzzy systems have the advantage of using a form of knowledge representation which in general is interpretable by human beings. However, unlike neural networks, fuzzy systems have no built-in learning mechanism. To compensate for this deficiency, genetic algorithms (GAs) have often been used to automate the design of fuzzy systems. Unfortunately, the inconsiderate use of GAs and the temptation to automate every conceivable aspect of the design can lead to the design of fuzzy systems with obscure structures that are not easily interpretable. In this chapter, we describe our efforts for constraining the use of genetic algorithms and evolutionary programming in order to design fuzzy logic controllers (fuzzy systems used in control applications) which are both accurate and interpretable.

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Author information

Authors and Affiliations

  1. School of Business IT, RMIT University, Melbourne, Victoria, 3000, Melbourne, Australia

    France Cheong

  2. Department of Computer Science and Computer Engineering, La Trobe University, Bundoora, Victoria, 3083, Australia

    Richard Lai

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  1. France Cheong
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  2. Richard Lai
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Editors and Affiliations

  1. Dpto. Ciencias de la Computación e Inteligencia Artificial, Escuela Técnica Superior de Ingeniería Informática, Universidad de Granada, E - 18071, Granada, Spain

    Jorge Casillas , Oscar Cordón  & Francisco Herrera ,  & 

  2. Dpto. Matemáticas Aplicadas a las Tecnologías de la Información, Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, E - 28040, Madrid, Spain

    Luis Magdalena

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Cheong, F., Lai, R. (2003). Constrained optimization of genetic fuzzy systems. In: Casillas, J., Cordón, O., Herrera, F., Magdalena, L. (eds) Accuracy Improvements in Linguistic Fuzzy Modeling. Studies in Fuzziness and Soft Computing, vol 129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-37058-1_3

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  • DOI: https://doi.org/10.1007/978-3-540-37058-1_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05703-8

  • Online ISBN: 978-3-540-37058-1

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