摘要
为充分利用遗传算法的全局搜索能力和BP算法的局部搜索能力,提出了基于遗传算法的遗传模糊神经网络模型,研究了故障特征参数模糊化处理和利用遗传算法优化神经网络权重的方法,加快了网络收敛速度,提高了收敛精度。在煤气鼓风机故障诊断中的应用表明,遗传模糊神经网络克服了BP算法中存在的网络学习收敛速度慢,以及容易陷入局部极小的问题,有效提高了故障诊断的精度。
In order to make full use of GA＇ s global searching and BP network＇s local searching, a genetic fuzzy neural network model is proposed. And the way of fault characteristic parameters＇ fuzzy processing and optimizing the weights and thresholds of ANN by GA are studied. As a result, the convergent rate and convergent precision are greatly increased. Application to the fault diagnosis of a gas blower system shows the new model overcomes the low learning rate and local optima of BP algorithm, and the fault diagnosis precision is effectively improved.
出处
《计算机工程与设计》
CSCD
北大核心
2008年第23期6079-6081,6093,共4页
Computer Engineering and Design
关键词
煤气鼓风机
模糊处理
神经网络
遗传算法
故障诊断
gas blower
fuzzy processing
neural network
genetic algorithm
fault diagnosis
