关键词: 地震预测, 前馈神经网络, 粒子群优化算法

Abstract: This paper proposed an earthquake prediction method based on neural networks and an improved particle swarm optimization algorithm. In this method, a feed forward neural network was applied to predict the level of earthquake, and a modified particle swarm optimization algorithm was applied to optimize the neural network model. In order to get weights of the optimal balance between the global search and local search, a Dynamic Mutational Particle Swarm Optimization (DMPSO) algorithm was designed by using the ideology of dynamic mutation. This algorithm was used to adjust weights of the feed forward neural network. The simulation results of the proposed method were compared with the simulation results of two feed forward networks with different training algorithms. The comparison results show that the prediction model with DMPSO has fastest convergence rate, the smallest prediction error and strongest generalization ability. In conclusion, the model with DMPSO is a good reference to the middle earthquake prediction.

Key words: earthquake prediction, Feed Forward Neural Network (FFNN), Particle Swarm Optimization (PSO) algorithm