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Sensitivity Analysis for Neural Networks

  • Daniel S. Yeung
  • Ian Cloete
  • Daming Shi
  • Wing W. Y. Ng

Part of the Natural Computing Series book series (NCS)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Daniel S. Yeung, Ian Cloete, Daming Shi, Wing W.Y. Ng
    Pages 1-15
  3. Daniel S. Yeung, Ian Cloete, Daming Shi, Wing W.Y. Ng
    Pages 17-24
  4. Daniel S. Yeung, Ian Cloete, Daming Shi, Wing W.Y. Ng
    Pages 25-27
  5. Daniel S. Yeung, Ian Cloete, Daming Shi, Wing W.Y. Ng
    Pages 29-31
  6. Daniel S. Yeung, Ian Cloete, Daming Shi, Wing W.Y. Ng
    Pages 33-46
  7. Daniel S. Yeung, Ian Cloete, Daming Shi, Wing W.Y. Ng
    Pages 47-53
  8. Daniel S. Yeung, Ian Cloete, Daming Shi, Wing W.Y. Ng
    Pages 55-67
  9. Daniel S. Yeung, Ian Cloete, Daming Shi, Wing W.Y. Ng
    Pages 69-82
  10. Back Matter
    Pages 83-86

About this book

Introduction

Artificial neural networks are used to model systems that receive inputs and produce outputs. The relationships between the inputs and outputs and the representation parameters are critical issues in the design of related engineering systems, and sensitivity analysis concerns methods for analyzing these relationships. Perturbations of neural networks are caused by machine imprecision, and they can be simulated by embedding disturbances in the original inputs or connection weights, allowing us to study the characteristics of a function under small perturbations of its parameters.

This is the first book to present a systematic description of sensitivity analysis methods for artificial neural networks. It covers sensitivity analysis of multilayer perceptron neural networks and radial basis function neural networks, two widely used models in the machine learning field. The authors examine the applications of such analysis in tasks such as feature selection, sample reduction, and network optimization. The book will be useful for engineers applying neural network sensitivity analysis to solve practical problems, and for researchers interested in foundational problems in neural networks.

Keywords

Adaline Backpropagation algorithm Hyperrectangle model Learning Multilayer perceptron (MLP) Neural networks Perceptron Perturbations Sensitivity analysis Supervised learning Unsupervised learning Vector learni machine learning optimization perception

Authors and affiliations

  • Daniel S. Yeung
    • 1
  • Ian Cloete
    • 2
  • Daming Shi
    • 3
  • Wing W. Y. Ng
    • 4
  1. 1.School of Computer Science &South China University of TechnologyGuangzhouChina, People's Republic
  2. 2.President's OfficeInternational University in GermanyBruchsalGermany
  3. 3.Dept. Electrical EngineeringKyungpook National UniversityDaeguKorea, Republic of (South Korea)
  4. 4.School of Computer Science &South China University of TechnologyGuangzhouChina, People's Republic

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-02532-7
  • Copyright Information Springer-Verlag Berlin Heidelberg 2010
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-02531-0
  • Online ISBN 978-3-642-02532-7
  • Series Print ISSN 1619-7127
  • Buy this book on publisher's site