摘要
针对全连接BP网络在解决大规模复杂问题时存在的收敛速度缓慢等问题,提出一种功能分区的BP网络结构模式.利用RBF神经元的物理特性对输入样本空间进行分解,并将分解后的样本送给不同的子BP网络学习.与全连接BP网络相比,降低了网络在学习过程中的权值搜索空间,提高了学习速度,改善了网络泛化性能,体现了人脑在学习过程中的知识积累特征.对三维墨西哥草帽函数逼近和双螺旋分类的实验结果表明,该网络能够解决全连接BP网络不能有效解决的问题.
For the problem that the fully coupled BP neural network suffers the slow convergence rate to solve the large scale complex problems,a structure model of function-dividing BP neural network architecture is presented.By using the physical characteristics of the RBF neurons,the input sample space is decomposed,and different sub-samples space is sent to different sub-module of BP neural network to learn automatically.Compared with the fully coupled BP neural network,the searching space of weight in the learning process of neural network is reduced,the learning speed and network＇s generalization performance are improved,and the characteristics of the human brain in the learning proces of knowledge accumulation are reflected.Experiments of 3D Mexican hat function approximation and two-spiral classification show that the neural network of function-dividing BP neural network can solve the problem that the fully coupled BP neural network can not solve perfectly.
出处
《控制与决策》
EI
CSCD
北大核心
2011年第11期1659-1664,共6页
Control and Decision
基金
国家自然科学基金项目(60873043)
国家"863"计划项目(2009AA04Z155)
北京市自然科学基金项目(4092010)
教育部博士点基金项目(200800050004)
北京市高等学校人才强教计划项目(PHR201006103)
关键词
BP神经网络
功能分区
权值搜索空间
知识积累
BP neural network
function-dividing
weight search space
knowledge accumulation
