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Evolutionary-Neuro-Fuzzy Control

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Intelligent Control

Part of the book series: Studies in Computational Intelligence ((SCI,volume 517))

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Abstract

It has been demonstrated that learning the shape of sigmoidal function can improve performance of neuro-fuzzy controller. Backpropagation learning algorithm does not include the parameter of the sigmoidal function shape. This chapter proposes the use of genetic algorithm to learn the weights, biases and shape of the sigmoidal function of the neural network simultaneously. The performance of the system using a neural network with a linear activation function seems to be better than neural network with a non-linear activation function.

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Notes

  1. 1.

    The term integrated is better suited here as the three technologies are in cooperative combination rather than hybrid as the term hybrid indicates some kind of amalgamated system in general.

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Authors and Affiliations

  1. School of Computing and Intelligent Systems, University of Ulster, Londonderry, UK

    Nazmul Siddique

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  1. Nazmul Siddique
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Correspondence to Nazmul Siddique .

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Siddique, N. (2014). Evolutionary-Neuro-Fuzzy Control. In: Intelligent Control. Studies in Computational Intelligence, vol 517. Springer, Cham. https://doi.org/10.1007/978-3-319-02135-5_8

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  • DOI: https://doi.org/10.1007/978-3-319-02135-5_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02134-8

  • Online ISBN: 978-3-319-02135-5

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