文献类型：会议论文

英文题名：Research on modeling of improved process neural network based on KPCA and discrete walsh transform

作者：Wang, Wen-Jia[1]; Luo, Jian-Xu[1]

机构：[1] Department of Automation, School of Information Science and Technology, ECUST, Shanghai, China

会议论文集：Proceedings - 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009

会议日期：December 11, 2009 - December 13, 2009

会议地点：Wuhan, China

语种：英文

摘要：Process Neural Network(PNN) has an important significance in solving industry modeling problems which are related to time, but long time is cost on high dimension inputs nonlinear modeling problems. A new Improved Process Neural Networks based on KPCA and Walsh (IPNN-KPW) are proposed in this paper. KPCA method and discrete Walsh transform are used to reduce process neural network's time cost. Momentum factor and self-adapting learning rate are adopted to accelerate the astringency of the network and keep down network's oscillation. The IPNN-KPW is applied to modeling of Polyacrylonitrile(PAN) average molecular weight in polymerization. The effectiveness of the algorithm is verified by the results. A higher accuracy of model is obtained with less time. ?2009 IEEE.