文献类型：期刊文献

中文题名：基于KPCA和离散Walsh变换的改进过程神经网络建模

英文题名：Modeling of Improved Process Neural Network Based on KPCA and Discrete Walsh Transform

作者：王文佳[1];罗健旭[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237

年份：2010

卷号：36

期号：4

起止页码：585

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：CSTPCD;;Scopus;北大核心:【北大核心2008】；CSCD:【CSCD2011_2012】；

基金：国家自然科学基金项目(64974066)

语种：中文

中文关键词：核主元分析;Walsh变换;过程神经网络;建模

外文关键词：KPCA; Walsh transform; process neural network; modeling

摘要：针对过程神经网络在输入维数较高时存在时间代价过大的缺点,提出了基于核主元分析(KPCA)和离散Walsh变换的改进过程神经网络算法(IPNN-KPW)。该算法结合KPCA和离散Walsh正交基变换,减少了过程神经网络的输入计算代价;引入动量因子和自适应学习率,加速了网络收敛并有效地抑制了网络震荡。应用该算法对聚合反应中聚丙烯腈平均分子量建模,仿真实验结果验证了该算法的有效性,它能以较少的时间代价得到较高的模型精度。
Process neural network could handle the modeling problems of time related industry, but it needs a long time when the input dimension is high. in this paper, a new improved process neural network based on KPCA and Watsh （IPNN-KPW） is proposed. Both KPCA method and discrete Walsh transform are used to reduce the time cost of process neural network. Meanwhile, both momentum factor and self- adapting learning rate are introduced to accelerate the astringency of the network and keep down network＇s oscillation. IPNN-KPW is applied to model polyacrylonitrile （PAN） average molecular weight in polymerization, whose results verify the effectiveness of the proposed algorithm.