Gauss Chaotic Neural Networks
We retrospect Chen’s chaotic neural network and then propose a new chaotic neural network model whose activation function is composed of Gauss and Sigmoid function. And the time evolution figures of the largest Lyapunov exponents of chaotic single neural units are plotted. Based on the new model, the model with different parameters is applied to combinational optimization problems. 10-city traveling salesman problem (TSP) is given to make a comparison between Chen’s and the new model with different parameters. Finally on the simulation results we conclude that the novel chaotic neural network model we proposed is more effective.
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