摘要
针对模糊神经网络控制器在应用误差反向传播算法训练时,易于陷入局部最优的问题,提出了一种将差分进化算法与BP算法相结合的学习法,首先利用差分进化算法的全局寻优能力,给BP算法一个好的寻优初始点;然后再以一定的概率进行BP算法的寻优。对一个带有滞后环节的二阶系统进仿真表明,控制性能优于基于BP的模糊神经网络控制器。
In order to avoid the local optimum resulted from back propagation algorithm, we present a novel training approach for fuzzy neural network controller, which combines differential evolution algorithm and BP algorithm. First, global optimization ability of DE is used to get good initial points for BP algorithm. Then BP algorithm is required at a certain probability, using its good local optimization ability. The approach is applied to a second order system with time delay. Simulation results indicate the efficiency of this approach.
出处
《计算机与应用化学》
CAS
CSCD
北大核心
2011年第12期1549-1552,共4页
Computers and Applied Chemistry
基金
国家自然科学基金资助项目(60974066)
关键词
模糊神经网络
差分进化
BP算法
fuzzy neural network, differential evolution algorithm, BP algorithm