摘要
提出了一种混合加权距离测量(weighted distance measure,weighted DM)参数的构建和训练RBF(radial basis function)神经网络的两步批处理算法.该算法在引进了DM系数参数的基础上,采用Newton法分别对径向基函数的覆盖参数、均值向量参数、加权距离测度系数以及输出权值进行了优化,并在优化过程中利用OLS(orthogonal least squares)法来求解Newton法的方程组.通过实验数据,不仅分析了Newton法优化的各个参数向量对RBF网络训练的影响,而且比较了混合优化加权DM与RLS-RBF(recursive least square RBF neural network)网络训练算法的收敛性和计算成本.所得到的结论表明整合了优化参数的加权DM-RBF网络训练算法收敛速度比RLS-RBF网络训练算法更快,而且具有比LM-RBF(Levenberg-Marquardt RBF)训练算法更小的计算成本,从而说明OLS求解的Newton法对优化RBF网络参数具有重要应用价值.
A hybrid two-step second-order batch approach is presented for constructing and training radial basis function（RBF）neural networks.Unlike other RBF neural network learning algorithms,the proposed paradigm uses Newton＇s method to train each set of network parameters,i.e.spread parameters,mean vector parameters and weighted distance measure（DM）coefficients and output weights parameters.For efficiently calculating the second-order equations of Newton＇s method,all the optimal parameters are found out using orthogonal least squares（OLS）with the multiply optimal learning factors（MOLFs）for training mean vector parameters.The simulation results of the proposed hybrid training algorithm on a real dataset are compared with those of the recursive least square based RBF（RLS-RBF）and Levenberg-Marquardt method based RBF（LM-RBF）training algorithms.Also,the analysis of the training performance for optimization of each set of parameters has been presented.The experimental results show that the proposed hybrid optimal weighted DM training algorithm,which is based on the optimization of the mean vectors,weighted DM coefficients and spread parameters,has significant improvement on training convergence speed compared with that of RLSRBF and has very less computation cost compared with that of LM-RBF.It confirms that Newton＇s method solved by OLS is a significantly valuable method for training the RBF neural network.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2015年第7期1477-1486,共10页
Journal of Computer Research and Development
基金
国家自然科学基金项目(51473088)
山东省科技攻关项目(2012GGE27069)
关键词
径向基函数神经网络
HESSIAN矩阵
NEWTON法
正交最小二乘法
网络参数优化
最优学习因子
radial basis function（RBF）neural network
Hessian matrix
Newton＇s method
orthogonal least squares（OLS）
weighted distance measure（weighted DM）
multiply optimal learning factors（MOLFs）
