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An Analysis on Minimum Searching Principle of Chaotic Neural Network
Masaya OHTA Kazumichi MATSUMIYA Akio OGIHARA Shinobu TAKAMATSU Kunio FUKUNAGA
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 1996/03/25
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section of Selected Papers from the 8th Karuizawa Workshop on Circuits and Systems)
chaos, neural network, minimum searching problem, attractor,
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This article analyzes dynamics of the chaotic neural network and minimum searching principle of this network. First it is indicated that the dynamics of the chaotic newral network is described like a gradient decent, and the chaotic neural network can roughly find out a local minimum point of a quadratic function using its attractor. Secondly It is guaranteed that the vertex corresponding a local minimum point derived from the chaotic neural network has a lower value of the objective function. Then it is confirmed that the chaotic neural network can escape an invalid local minimum and find out a reasonable one.