A Parameters Optimization of Synergetic Neural Network Based on Differential Evolution Algorithm

Synergetic neural network (SNN) is a top-down network to explain the phase transition and self-organization in non-equilibrium system. The network parameters have a crucial impact on the recognition performance of synergetic neural network. At present, there is no good way to control and adjust the network parameters. To solve these problems, an improved parameters optimization algorithm based on differential evolution algorithm is proposed and implemented in this paper. There are two main works in this paper. Firstly, a semantic analysis model based on synergetic neural network is presented. Secondly, differential evolution algorithm is used to search the global optimum of network parameters in the corresponding parameter space. The experiments showed that the optimization algorithm can improve the synergetic recognition performance.