摘要
针对传统遗传算法收敛速度慢的缺点,本文将改进后的遗传算法应用于RBF神经网络,对隐层中心和宽度值进行同步优化,并在复杂非线性函数的逼近实验中证明了本文算法相比传统遗传算法在搜索全局最小点的速度上得到了很大提高.
Adaptive probability of crossover and mutation are used in this paper and they can adjust themselves according to the fitness function, so that this approach can solve the problem that the traditional genetic algorithm converges into minimum points slowhy. We optimize the centers and widths of RBF neural network constructed in the method this paper presented. The experiments of the approximation of nonlinear function indicate that the speed in sddking minimum points of this method is greatly improved compared with that of traditional genetic algorithm.
出处
《山东师范大学学报（自然科学版）》
CAS
2007年第3期37-39,共3页
Journal of Shandong Normal University(Natural Science)
关键词
RBF神经网络
自适应遗传算法
函数逼近
RBF neural network
adaptive genetic algorithm
approximation of function
