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The Macronet Element: A Substitute for the Conventional Neuron

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Developments in Applied Artificial Intelligence (IEA/AIE 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2358))

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Abstract

A potential replacement for the conventional neuron is introduced. This is called a Macronet element and uses multiple channels per signal path with each channel containing two trainable non-linear structures in addition to a conventional weight. The authors show that such an architecture provides a rich spectrum of higher order powers and cross products of the inputs using less weights than earlier higher order networks. This lower number of weights does not compromise the ability of the Macronet element to generalise. Results from training a Macronet element to develop a relationship from a sparse map of Europe are given.

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Author information

Authors and Affiliations

  1. Centre for Intelligent Systems and Complex Processes, School of Biophysical Sciences and Electrical Engineering Swinburne University, VIC, 3122, Australia

    Tim Hendtlass & Gerrard Murray

Authors
  1. Tim Hendtlass
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  2. Gerrard Murray
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Editor information

Editors and Affiliations

  1. Centre for Intelligent Systems and Complex Processes, Swinburne University of Technology, John Street, Hawthorn, Victoria, Australia, 3122

    Tim Hendtlass

  2. Department of Computer Science, Southwest Texas State University, 601 University Drive, San Marcos, TX, 78666, USA

    Moonis Ali

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© 2002 Springer-Verlag Berlin Heidelberg

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Hendtlass, T., Murray, G. (2002). The Macronet Element: A Substitute for the Conventional Neuron. In: Hendtlass, T., Ali, M. (eds) Developments in Applied Artificial Intelligence. IEA/AIE 2002. Lecture Notes in Computer Science(), vol 2358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48035-8_21

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  • DOI: https://doi.org/10.1007/3-540-48035-8_21

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43781-9

  • Online ISBN: 978-3-540-48035-8

  • eBook Packages: Springer Book Archive

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