Accelerated Optimal Topology Search for Two-hidden-layer Neural Networks

Alan Thomas, Simon Walters, Miltiadis Petridis, Mohammad Malekshahi Gheytassi, Robert Morgan

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBN

Abstract

Two-hidden-layer feedforward neural networks are investigated for the existence of an optimal hidden node ratio. In the experiments, the heuristic n1 =int (0.5nh + 1), where n1 is the number of nodes in the first hidden layer and nh is the total number of hidden nodes, found networks with generalisation errors, on average, just 0.023%-0.056% greater than those found by exhaustive search. This reduced the complexity of an exhaustive search from quadratic, to linear in , with very little penalty. Further reductions in search complexity to logarithmic could be possible using existing methods developed by the Authors
Original languageEnglish
Title of host publicationEngineering Applications of Neural Networks. EANN 2016
Place of PublicationSwitzerland
PublisherSpringer International Publishing
Pages253-266
Number of pages14
ISBN (Electronic)9783319441887
ISBN (Print)9783319441870
DOIs
Publication statusPublished - 19 Aug 2016
EventEngineering Applications of Neural Networks. EANN 2016 - Aberdeen, UK, 2-5 September, 2016
Duration: 19 Aug 2016 → …

Publication series

NameCommunications in Computer and Information Sciences

Conference

ConferenceEngineering Applications of Neural Networks. EANN 2016
Period19/08/16 → …

Bibliographical note

The final publication is available at link.springer.com

Keywords

  • Two-hidden-layer feedforward
  • ANN
  • Exhaustive search
  • Optimal topology
  • Optimal node ratio
  • Heurix
  • Universal function approximation

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  • Cite this

    Thomas, A., Walters, S., Petridis, M., Malekshahi Gheytassi, M., & Morgan, R. (2016). Accelerated Optimal Topology Search for Two-hidden-layer Neural Networks. In Engineering Applications of Neural Networks. EANN 2016 (pp. 253-266). (Communications in Computer and Information Sciences). Springer International Publishing. https://doi.org/10.1007/978-3-319-44188-7_19