Abstract
In order to get better control of the chaotic randomness and improve the convergence rate of Transient Chaotic Neural Networks(TCNN),Effective improvement is proposed in this study.By modifying the Incentive function with composite sine sigmoid function and Time-varying Gain are adopted in the function, and two-stage annealing mechanism is applied in the annealing process of the dynamic equations.Empirical results show that combination of the two aspects has a better control of the two processes of the chaotic state,improved algorithm obtains a wealth of chaotic characteristics. Experiments on the channel allocation problem indicate that the modified algorithm can better solve this kind of Combinatorial optimization problems. Results are compared with other prior algorithms such as basic TCNN and only subsection of TCNN.The comparison shows that the performance of the algorithm has been greatly improved, local optima is avoided, and the convergence speed is improved more than 14%.
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