Staged training of Neocognitron by evolutionary algorithms

Z. Pan, T. Sabisch, R.G. Adams, H. Bolouri

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

6 Citations (Scopus)
29 Downloads (Pure)

Abstract

The Neocognitron, inspired by the mammalian visual system, is a complex neural network with numerous parameters and weights which should be trained in order to utilise it for pattern recognition. However, it is not easy to optimise these parameters and weights by gradient decent algorithms. In this paper, we present a staged training approach using evolutionary algorithms. The experiments demonstrate that evolutionary algorithms can successfully train the Neocognitron to perform image recognition on real world problems.
Original languageEnglish
Title of host publicationIn: Procs of the 1999 Congress on Evolutionary Computation (CEC'99) Vol. 3
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1965-1972
ISBN (Print)0780355369
DOIs
Publication statusPublished - 1999

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