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Sensitivity Analysis of Radial-Basis Function Neural Network due to the Errors of the I.I.D Input

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Machine Learning and Cybernetics (ICMLC 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 481))

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Abstract

An important issue, in the design and implementation of a Radial-Basis Function Neural Network (RBFNN), is the sensitivity of its output to input perturbations. Based on the central limit theorem, this paper proposes a method to compute the sensitivity of the RBFNN due to the errors of the inputs of the networks. For simplicity and practicality, all inputs are assumed to be independent and identically distributed (i.i.d.) with uniform distribution on interval (a, b). A number of simulations are conducted and the good agreement between the experimental results and the theoretical results verifies the reliability and feasibility of the proposed method. With this method, not only the relationship among the sensitivity of RBFNN, input error ratios and the number of the neurons of the input layer but also the relationship among the sensitivity, input error ratios and the number of the neurons of the hidden layer is founded.

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Author information

Authors and Affiliations

  1. Faculty of Mathematics and Computer Science, Hebei University, Baoding, 071002, China

    Jie Li & Ying Liu

  2. Department of computer science, Communication training base of general staff, Xuanhua, 075100, China

    Jun Li

Authors
  1. Jie Li
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  2. Jun Li
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  3. Ying Liu
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Corresponding author

Correspondence to Jie Li .

Editor information

Editors and Affiliations

  1. Hebei University, Baoding, China

    Xizhao Wang

  2. Department of Electrical and Computer En, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  3. South China University of Technology, Guangzhou, China

    Patrick Chan

  4. Hebei University, Baoding, China

    Qiang He

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© 2014 Springer-Verlag Berlin Heidelberg

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Li, J., Li, J., Liu, Y. (2014). Sensitivity Analysis of Radial-Basis Function Neural Network due to the Errors of the I.I.D Input. In: Wang, X., Pedrycz, W., Chan, P., He, Q. (eds) Machine Learning and Cybernetics. ICMLC 2014. Communications in Computer and Information Science, vol 481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45652-1_35

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  • DOI: https://doi.org/10.1007/978-3-662-45652-1_35

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45651-4

  • Online ISBN: 978-3-662-45652-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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