Minimal disturbance back-propagation algorithm

Greg L. Heileman, Michael Georgiopoulos, Harold K. Brown

Research output: Contribution to conferencePaperpeer-review

Abstract

Summary form only given, as follows. A novel learning algorithm for multilayered neural networks is presented. This algorithm, called minimal disturbance backpropagation, approximates a least mean squared error minimization of the error function while minimally disturbing the connection weights in the network. This means that the information previously trained into the network is disturbed to the smallest amount possible while achieving the desired error correction. Simulation results indicate that this algorithm is more robust and yields much faster convergence rates than the standard backpropagation algorithm.

Original languageEnglish (US)
Pages625
Number of pages1
StatePublished - 1989
Externally publishedYes
EventIJCNN International Joint Conference on Neural Networks - Washington, DC, USA
Duration: Jun 18 1989Jun 22 1989

Other

OtherIJCNN International Joint Conference on Neural Networks
CityWashington, DC, USA
Period6/18/896/22/89

ASJC Scopus subject areas

  • Engineering(all)

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