修正初始权值的BP网络在CSTR故障诊断中的应用  

Application of BP Network with Changing Initial Weights to CSTR Fault Diagnosis

作　　者：江艳君[1] 李柠[1] 黄道[1] 

机构地区：[1]华东理工大学自动化工程中心,上海200237

出　　处：《华东理工大学学报（自然科学版）》2004年第2期207-210,共4页Journal of East China University of Science and Technology

基　　金：国家863资助项目(2002AA412120)

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.

关 键 词：神经网络 初始权值 BP算法 复合法 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]