计算机应用研究2011，Vol.28Issue(1)：75-77,3.DOI:10.3969/j.issn.1001-3695.2011.01.019
基于样条插值函数的离散过程神经网络训练
Discrete process neural networks training based on spline function
摘要
Abstract
To solve the problem of process neural networks ( PNN ) can not receive discrete samples, this paper proposed the discrete PNN training algorithm based on piecewise spline interpolation function.Firstly ,divided sampling interval into several sub-intervals by sampling points, and then constructed the piecewise spline functions of samples and weights in the sampling interval.Secondly ,computed the integrals of the product functions of samples and weights functions in the sampling interval,and submitted to process neurons in hide layer.Finally ,obtained the PNN output in output layer.By using a linear spline ,quadratic spline, and cubic spline function, designed three different algorithms respectively.The experimental results show that the linear spline has higher computation efficiency and lower approximation ability, the cubic spline has lower computation efficiency and higher approximation ability,the quadratic spline is ideal both in the computation efficiency and approximation abihty.Hence, quadratic spline function is a better choice for discrete process neural networks.关键词
过程神经网络/样条函数/网络训练/算法设计分类
计算机与自动化引用本文复制引用
李盼池,王海英..基于样条插值函数的离散过程神经网络训练[J].计算机应用研究,2011,28(1):75-77,3.基金项目
中国博士后基金特别资助项目(201003405) （201003405）
中国博士后基金资助项目(20090460864) （20090460864）
黑龙江省博士后基金资助项目(LBH-Z09289) （LBH-Z09289）
黑龙江省教育厅科学技术研究项目(11551015,11551017) （11551015,11551017）
