Evolving Neural Networks with Iterative Learning Scheme for Associative Memory

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dc.creatorFujita, Sh.
dc.creatorNishimura, H.
dc.date1995-08-02
dc.date.accessioned2026-07-07T09:05:32Z
dc.date.available2026-07-07T09:05:32Z
dc.descriptionA 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.
dc.description9 pages, compressed and uuencoded postscript file
dc.identifierhttps://arxiv.org/abs/adap-org/9507008
dc.identifierhttp://arxiv.org/abs/adap-org/9507008
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/149710
dc.subjectAdaptation and Self-Organizing Systems
dc.subjectQuantitative Biology
dc.titleEvolving Neural Networks with Iterative Learning Scheme for Associative Memory
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