摘要
基于粒子群优化算法,将灰色关联分析法应用到BP神经网络隐层结点数的确定,实现了BP神经网络结构的优化;然后将贝叶斯正则法应用于神经网络训练,进一步提高网络的泛化性能。仿真结果表明泛化能力明显优于其他改进的BP算法,拟合效果较好。
Based on Particle Swarm Optimization（PSO）,by using the grey correlation analysis on the hidden node number＇s determination of BP nueral network,this method realizes the optimal of BP nueral network.Bayesian inference methods are applied to the training of feed-forward neural networks in order to improve their generalization capabilities.The results show that the algorithm has better generalization capacity than other improved BP algorithms,and furthermore,it has better effects of approximation.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第11期37-38,42,共3页
Computer Engineering and Applications
基金
江西省自然科学基金No.2008GZS0076~~
关键词
粒子群优化
灰色关联分析
贝叶斯推理
神经网络
particle swarm optimization
grey correlation analaysis
Bayesian inference
nueral network
