计算机工程2012，Vol.38Issue(10)：144-147,4.DOI:10.3969/j.issn.1000-3428.2012.10.044
基于DFP校正拟牛顿法的傅里叶神经网络
Fourier Neural Network Based on DFP Emendatory Quasi-Newton Method
摘要
Abstract
This paper proposes a novel Fourier Neural Network(FNN) based on DFP emendatory Quasi-Newton method in dealing with the problems of local minimum, slow learning rate and poor generalization ability of the FNN based on steepest descent method. The newly FNN has low computational complexity, good generalization ability and global optimization. Two numerical examples are utilized to validate the proposed learning algorithm by comparing with BP neural network and two kinds of FNNs. Numerical example results show that the computational complexity is 5% of the steepest descent method's and 80% of the least squares method's, and the new learning algorithm has good generalization capacity.关键词
傅里叶神经网络/BP神经网络/最速下降法/最小二乘法/拟牛顿法/DFP校正拟牛顿法Key words
Fourier neural network, BP neural network/steepest descent method/least squares method/Quasi-Newton method/DFP emendatory Quasi-Newton method分类
信息技术与安全科学引用本文复制引用
林琳,黄南天,高兴泉..基于DFP校正拟牛顿法的傅里叶神经网络[J].计算机工程,2012,38(10):144-147,4.基金项目
吉林省科技发展计划基金资助项目(2009148) （2009148）
吉林省教育厅"十二五"科学技术研究基金资助项目(2011262) （2011262）
