Self-organization in Probabilistic Neural Networks

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  • 確率的ニューラルネットワークにおける自己組織化
  • カクリツテキ ニューラル ネットワーク ニ オケル ジコ ソシキカ

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Abstract

Stuart Kauffman explored the law of self-organization in random Boolean networks, and Kosaku Inagaki also did it in neural networks partially. The aim of this paper is to show that probabilistic neural networks (PNNs), which are recurrent networks and are controlled by a probabilistic transision rule based on Boltzh-mann machine, hold the order, even though we determine the weights, the thresholds, and the connections between neurons randomly. And, we also studied the deterministic transient neural networks which are the special networks of PNNs.

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