基于遗传算法的广义回归神经网络在地震预测中的应用

Application of Generalized Regression Neural Network Based on Genetic Algorithm in Earthquake Predication

提出了利用基于遗传算法结合广义回归神经网络(GRNN)进行地震预测的新方法。利用遗传算法的全局搜索能力、不易陷入局部极小点等优点来优化GRNN的径向基函数中心、宽度以及输出层的权值,使得计算结果全局最优。在实际地震数据中选取了100组样本数据进行仿真实验,并利用MATLAB进行仿真。仿真结果表明,本文提出的方法具有较高的精度和一定的理论指导意义。

A new method was proposed to predict earthquake through using generalized regression neural network（GRNN） based on genetic algorithm（GA）. Global optimum can be found through using GA＇s a- bility of global searching and being not liable to get the minimum to optimize the the center and width of radial basis function and weights of output layer. At last, using100 sample data obtained from actual earthquake, a simulation experiment is done based on MATLAB. The simulation shows that the method has a good result.