A study on the mechanism of the minimum searching by the chaotic neural network

This article analyzes dynamics of the chaotic neural network and its minimum searching principle. First, it is indicated that the dynamics of the chaotic neural network is described as a gradient descent method, and it is clarified that the behavior of the chaotic neural network can not only catch a local minimum of the energy but also escape from a local minimum without using any special technique. The performance of the chaotic behavior is then evaluated experimentally. In order to compare the chaotic behavior, a random minimum searching algorithm is provide. It is confirmed that chaos is more effective than random behavior from experimental results