Performance of multilayer neural networks in binary-to-binary mappings under weight errors

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

In this research, the probability of misclassification error of a multilayer neural network used in binary-to-binary mappings is derived. The connection weights, determined through training, are assumed to be subject to an additive, random, normally distributed error. The probability of misclassification is also derived through simulation of example application. The simulation results and the theoretical results are shown to match very closely. Our results and the theoretical results are shown to match closely. Our results give predictability to NN performance and allow for changing NN design parameters, such as weight vectors and number of nodes, in order to obtain a certain tolerance to weight errors.

Original languageEnglish (US)
Title of host publication1993 IEEE International Conference on Neural Networks
PublisherPubl by IEEE
Pages1684-1689
Number of pages6
ISBN (Print)0780312007
StatePublished - Jan 1 1993
Event1993 IEEE International Conference on Neural Networks - San Francisco, California, USA
Duration: Mar 28 1993Apr 1 1993

Publication series

Name1993 IEEE International Conference on Neural Networks

Other

Other1993 IEEE International Conference on Neural Networks
CitySan Francisco, California, USA
Period3/28/934/1/93

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Control and Systems Engineering
  • Software
  • Artificial Intelligence

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