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A Simple Training Law Suitable for On-Chip Learning.

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ICANN ’93 (ICANN 1993)

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Abstract

A very simple and efficient method for artificial neural networks training is proposed. Extensive simulation has established that it works very well as an ANN training law. It is faster than BackPropagation, it is suitable for on-chip learning and it can be implemented in parallel computers very easily.

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References

  1. Jacobs, R.A. (1988). Increased rates of Convergence through Learning Rate Adaptation. Neural Networks, vol.1. pp 295–307

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Author information

Authors and Affiliations

  1. Dept of Electrical Engineering.Faculty of Engineering, Aristotle University of Thessaloniki, BOX 438, 54006, Thessaloniki, Greece

    V. Petridis & K. Paraschidis

Authors
  1. V. Petridis
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  2. K. Paraschidis
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Editor information

Editors and Affiliations

  1. Dutch Foundation for Neural Networks, University of Nijmegen, Geert Grooteplein 21, 6525 EZ, Nijmegen, The Netherlands

    Stan Gielen  & Bert Kappen  & 

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© 1993 Springer-Verlag London Limited

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Petridis, V., Paraschidis, K. (1993). A Simple Training Law Suitable for On-Chip Learning.. In: Gielen, S., Kappen, B. (eds) ICANN ’93. ICANN 1993. Springer, London. https://doi.org/10.1007/978-1-4471-2063-6_315

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  • DOI: https://doi.org/10.1007/978-1-4471-2063-6_315

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  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-19839-0

  • Online ISBN: 978-1-4471-2063-6

  • eBook Packages: Springer Book Archive

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