Abstract:

In order to improve the prediction accuracy of network security situation based on radical basis function (RBF) neural network, an optimization algorithm of RBF neural network based on affinity propagation (AP) clustering and differential evolution (DE) is proposed. Firstly, the AP clustering is used to optimize the center and the number of the hidden layer. Secondly, AP clustering is used to get the population diversity (PD), the scaling factor and the crossover probability of DE are adaptively changed with the PD for the optimized width and connection weights of RBF neural network. In order to avoid falling into the local optimum and jump out of the local extreme point, the elite individual and PD’ centers of each generation population are searched by chaotic search. The simulation results show that the APDERBF algorithm can enhance the generalization ability, and it also has high prediction accuracy for the network security situation.