多重ブランチをもつ一般化学習ネットワークにおけるカオス制御

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  • Chaos Control on Multi-Branch Universal Learning Network
  • タジュウ ブランチ オ モツ イッパンカ ガクシュウ ネットワーク ニ オケル

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The authors already proposed Chaos Control Method on Universal Learning Network (U. L. N.). The method can control chaotic phenomena on such U. L. N. that have bounded functional nodes. In this paper, the chaos control is extended to multi-branch U. L. N. for the purpose of investigating the influence of increasing the number of branches between nodes and changing delay times. Generation and die-out of chaotic phenomena can be realized by changing Lyapunov Number of U. L. N. to a positive and negative value respectively by U. L. N. parameter learning. The parameter learning is carried out by using a gradient method with a newly devised calculation algorithm of second order derivatives of U. L. N. and a random search method which stabilizes the learning procedure. In the simulations of multi-branch U. L. N., the results show that, in the case of generating chaotic phenomena, the Lyapunov Number can be more quickly changed to the positive desired value on the multi-branch U. L. N. than on the single-branch U. L. N., that is, the ordinary recurrent neural network. The results also show that the larger the time delay is, the more difficult changing the Lyapunov Number to the desired positive value is, in other words, generating chaotic phenomena becomes more difficult. Based on the simulation results of the single-branch U. L. N., namely the ordinary recurrent neural network and the multi-branch U. L. N., suitable U. L. N. parameters and structures have been made clear that can realize generation and die-out of chaotic phenomena more efficiently.

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