Departmental Bulletin Paper 相互結合型カオスニューラルネットワークを用いた二次割当問題に対する最適化技法におけるカオスニューロンの解析(学内特別研究および国外研修)--(学内特別研究費報告書)

松浦, 隆文  ,  Takafumi, Matsuura

The quadratic assignment problem (QAP) is one of the most famous combinatorial optimization problems which belong to NP-hard. To solve the QAP, a method which uses mutually-connected chaotic neural network (CNN) has already been proposed. In the method, chaotic dynamics of the CNN effectively controls to avoid the local minima and to search optimal or near-optimal solutions. However, it is not so easy to generate feasible solutions from the CNN, because an output of a chaotic neuron takes an analog value. To generate a feasible solution from the CNN, a solution decision method has already been proposed. In this paper, to generate a better solution from the CNN, we analyze an inter spike interval of the chaotic neurons by using statistical measures such as a coefficient of variation and a local variation, which are frequently used in the field of neuroscience.

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