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Modeling and Synthesis of Computational Efficient Adaptive Neuro-Fuzzy Systems Based on Matlab

  • Guillermo Bosque
  • Javier Echanobe
  • Inés del Campo
  • José M. Tarela
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5164)

Abstract

New potential applications for neural networks and fuzzy systems are emerging in the context of ubiquitous computing and ambient intelligence. This new paradigm demands sensitive and adaptive embedded systems able to deal with a large number of stimulus in an efficient way. This paper presents a design methodology, based on a new Matlab tool, to develop computational-efficient neuro-fuzzy systems. To fulfil this objective, we have introduced a particular class of adaptive neuro-fuzzy inference systems (ANFIS) with piecewise multilinear (PWM) behaviour. Results obtained show that the PWM-ANFIS model generates computational-efficient implementations without loss of approximation capabilities or learning performance. The tool has been used to develop both software and hardware approaches as well as special architectures for hybrid hardware/software embedded systems.

Keywords

Neuro-fuzzy systems ANFIS model neuro-fuzzy CAD tool function approximation Matlab environment embedded systems 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Guillermo Bosque
    • 1
  • Javier Echanobe
    • 2
  • Inés del Campo
    • 2
  • José M. Tarela
    • 2
  1. 1.Department of Electronics and TelecommunicationsUniversity of the Basque CountryBilbaoSpain
  2. 2.Department of Electricity and ElectronicsUniversity of the Basque CountryLeioaSpain

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