Wavelet chaotic neural networks and their application to continuous function optimization

Neural networks have been shown to be powerful tools for solving optimization problems. In this paper, we first retrospect Chen’s chaotic neural network and then propose several novel chaotic neural networks. Second, we plot the figures of the state bifurcation and the time evolution of most positive Lyapunov exponent. Third, we apply all of them to search global minima of continuous functions, and respectively plot their time evolution figures of most positive Lyapunov exponent and energy function. At last, we make an analysis of the performance of these chaotic neural networks.