计算机工程与应用2012，Vol.48Issue(29)：55-58,113,5.DOI:10.3778/j.issn.1002-8331.2012.29.011
采用双折线步方法的傅里叶神经网络
Novel Fourier neural network using double dogleg step method
摘要
Abstract
Neural network has been one of effective tools in dealing with non-linear system recognition problem. However, the common multilayer perceptron has some faults, such as instability, and low convergence velocity. Based on multilayer perceptron and Fourier series, a kind of neural network, named Fourier neural network, is proposed. Compared with traditional multilayer perceptron, Fourier neural network has better pattern classification ability and generalization property. Since the existent Fourier neural network adopts method of steepest descent which induces the problems of local minimum and low learning velocity, it constructs an improved Fourier neural network based on the double dogleg step method and utilizes this network in dealing with non-linear system recognition problem. The double dogleg step method avoids the local minimum problem and has two-order convergence velocity. Several simulation examples are utilized to validate the performance of the improved network, and the results are compared with some results obtained from several other classical neural networks. Meanwhile the new network is applied to solve non-linear system recognition problem compared with results from other methods.关键词
非线性系统辨识/傅里叶神经网络/最速下降法/双折线步方法Key words
non-linear system recognition/ Fourier neural network/ method of steepest descent/ double dogleg step 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..采用双折线步方法的傅里叶神经网络[J].计算机工程与应用,2012,48(29):55-58,113,5.基金项目
吉林省科技发展计划项目(No.2009148) （No.2009148）
吉林省教育厅"十二五"科学技术研究项目(No.2011262). （No.2011262）
