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Evolving neural networks with iterative learning scheme for associative memory

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Abstract

A locally iterative learning (LIL) rule is adapted to a model of the associative memory based on the evolving recurrent-type neural networks composed of growing neurons. There exist extremely different scale parameters of time, the individual learning time and the generation in evolution. This model allows us definite investigation on the interaction between learning and evolution. And the reinforcement of the robustness against the noise is also achieved in the evolutional scheme.

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Authors and Affiliations

  1. Graduate School of Science and Technology, Kobe University Rokkodai, Nada, 657, Kobe, Japan

    Shigetaka Fujita

  2. Department of Information Science, Hyogo University of Education, Yashiro-cho, 673-14, Hyogo, Japan

    Haruhiko Nishimura

Authors
  1. Shigetaka Fujita
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  2. Haruhiko Nishimura
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Fujita, S., Nishimura, H. Evolving neural networks with iterative learning scheme for associative memory. Neural Process Lett 2, 1–5 (1995). https://doi.org/10.1007/BF02279930

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