摘要
针对BP算法学习神经网络权值收敛速度慢、易陷入局部最优等缺点 ,采用遗传算法学习BP网络的权值 ;并给出了“异或”(XOR)问题的仿真示例 ,通过比较 ,发现遗传算法不会陷入局部最优 。
Aimed at the shortcoming of back propagation algorithm such as slow convergence speed of the weights during learning process, easy to be trapped into local optimum, etc, a genetic algorithm is adopted to learn the weights in BP neural network and, moreover, a simulation illustration of 'XOR' problem is given. It is found by comparison that genetic algorithm can avoid the trap of local optimum and effectively improve the convergence speed.
出处
《甘肃工业大学学报》
北大核心
2001年第2期20-22,共3页
Journal of Gansu University of Technology
关键词
遗传算法
神经网络
BP算法
权值
genetic algorithm
neural network
BP algorithm
weight