文献类型：期刊文献

中文题名：修正初始权值的BP网络在CSTR故障诊断中的应用

英文题名：Application of BP Network with Changing Initial Weights to CSTR Fault Diagnosis

作者：江艳君[1];李柠[1];黄道[1]

机构：[1]华东理工大学自动化工程中心,上海200237

年份：2004

卷号：30

期号：2

起止页码：207

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;;EI(收录号：2004268239456);Scopus;北大核心:【北大核心2000】；CSCD:【CSCD2011_2012】；

基金：国家863资助项目(2002AA412120)

语种：中文

中文关键词：神经网络;初始权值;BP算法;复合法

外文关键词：neural networks; initial weight; BP algorithm; compositional method

摘要：将BP算法和使用复合法修正初始权值的BP算法运用到CSTR模型中进行故障诊断。采用复合法对初始权值进行修改,避免了BP算法中初始权值的随机性带来的收敛缓慢甚至瘫痪现象,并结合CSTR模型的故障诊断进行了仿真运算,与BP网络的比较表明了改进算法在运算效率上的优势。
Neural networks have been widely used in kinds of research fields. In this paper, faults of CSTR will be detected and diagnosed using an improved BP algorithm. Due to remarkable influence of initial weights on networks' training speed, great attention is paid to selection of initial weights. A compositional method is used to modify initial weights in order to avoid the low convergence and system paralysis caused by the randomicity of initial weights. The fault diagnosis of CSTR model is simulated to indicate higher performance of the improved algorithm compared with BP networks.