基于遗传模拟退火算法优化的BP神经网络

摘要

在研究标准BP神经网络的基础上,针对其存在的收敛速度慢、且容易陷入局部极小值等问题进行分析,设计实现一种采用数值优化的方法来改进BP网络性能的新的混合神经网络模型.通过引入遗传模拟退火算法扩大了网络的权值更新空间,把得到最优权值赋予BP神经网络,从而使优化后的神经网络具有泛化性好,不易陷入局部极小值等优点.与标准BP神经网络进行比较,仿真结果表明,该网络模型能够达到比较高的分类精度.%After studying the disadvantage of BP neural network which has low convergent speed and trap into local minima easily,an idea of designing a new hybrid neural network model which adopts the method of numerical optimization is presented. By using Genetic-Stimulated Annealing algorithm (GSA), expands the updated space of weight. On the basis, it makes the acquired better value as the weight of BP neural network, and the optimized BP network is not easy to trap into the local minima and has good generalization characteristic. Making the comparation GSA network with standard BP network, simulation analysis demonstrates that this network model can attain higher categories of precision.