【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。更多还原

【Abstract】 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.更多还原