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Approximation Using Fuzzy Perturbed Neural Networks

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Intelligent Systems II: Complete Approximation by Neural Network Operators

Part of the book series: Studies in Computational Intelligence ((SCI,volume 608))

Abstract

This chapter deals with the determination of the rate of convergence to the unit of each of three newly introduced here fuzzy perturbed normalized neural network operators of one hidden layer. These are given through the fuzzy modulus of continuity of the involved fuzzy number valued function or its high order fuzzy derivative and that appears in the right-hand side of the associated fuzzy Jackson type inequalities.

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Authors and Affiliations

  1. Department of Mathematical Sciences, University of Memphis, Memphis, TN, USA

    George A. Anastassiou

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  1. George A. Anastassiou
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Correspondence to George A. Anastassiou .

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Anastassiou, G.A. (2016). Approximation Using Fuzzy Perturbed Neural Networks. In: Intelligent Systems II: Complete Approximation by Neural Network Operators. Studies in Computational Intelligence, vol 608. Springer, Cham. https://doi.org/10.1007/978-3-319-20505-2_26

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  • DOI: https://doi.org/10.1007/978-3-319-20505-2_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20504-5

  • Online ISBN: 978-3-319-20505-2

  • eBook Packages: EngineeringEngineering (R0)

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