Prediction Error of a Fault Tolerant Neural Network

  • John Sum
  • Chi-sing Leung
  • Kevin Ho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4232)


For more than a decade, prediction error has been one powerful tool to measure the performance of a neural network. In this paper, we extend the technique to a kind of fault tolerant neural network. Consider a neural network to be suffering from multiple-node fault, a formulae similar to that of Generalized Prediction Error has been derived. Hence, the effective number of parameter of such a fault tolerant neural network is obtained. A difficulty in obtaining the mean prediction error is discussed and then a simple procedure for estimation of the prediction error empirically is suggested.


Neural Network IEEE Transaction Prediction Error Radial Basis Function Fault Tolerance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • John Sum
    • 1
    • 2
  • Chi-sing Leung
    • 2
  • Kevin Ho
    • 3
  1. 1.Department of Information ManagementChung Shan Medical UniversityTaichungTaiwan
  2. 2.Department of Electronic EngineeringCity University of Hong KongKowloon TongHong Kong
  3. 3.Department of Computer Science and Communication EngineeringProvidence UniversitySha-LuTaiwan

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