摘要
为了解决径向基函数(RBF)神经网络权值与结构难以确定的问题,基于权值直接确定法,及隐层神经元中心、方差、数目与神经网络性能的关系,提出一种边增边删型的网络权值与结构双确定法。在此方法基础之上,构建一种RBF神经网络分类器并探讨其分类性能和抗噪能力。计算机数值实验结果验证所提出的边增边删型的权值与结构双确定法能够快速、有效地确定网络的中心、方差和网络最优的权值与结构,所构造的模式分类器具有优越的分类性能和抗噪能力。
In order to solve the difficulties in determining the weights and structure of the radial basis function （RBF） neural network.Based on the weights-direct-determination （WDD）method and the relationship among centers,variances, the number of hidden-layer neurons and the performance of the neural network,a pruning-while-growing-type weights-and-structure-determination （PWGT-WASD）algorithm is proposed.On the basis of the PWGT-WASD algorithm,a kind of RBF neural network classifier is constructed,and its classifying and antinoise ability is further discussed in this paper.Com-puter numerical experiment results substantiate that the proposed PWGT-WASD algorithm can determine the centers,the va-riances and the optimal weights and structure of RBF neural network quickly and effectively.The constructed RBF pattern classifier has the superiority in terms of classification and antinoise ability.
出处
《计算技术与自动化》
2014年第3期1-7,共7页
Computing Technology and Automation
基金
国家自然科学基金项目(61075121和60935001)
教育部高等学校博士学科点专项科研基金博导类课题(20100171110045)
关键词
RBF神经网络
模式分类器
边增边删型
权值与结构双确定法
抗噪性
RBF neural network
pattern classifier
pruning-while-growing-type
weights-and-structure-determination algorithm
antinoise ability
