摘要
提出一种基于粗糙集和遗传算法的BP神经网络故障诊断方法,解决基本BP网络收敛速度慢、精度低、易陷入局部极小值问题。运用粗糙集理论对训练样本进行属性约简,简化BP网络输入维数。设计2次遗传算法训练BP网络,第一次优化神经网络隐含层节点个数,第二次在神经网络结构确定的情况下,优化网络连接权值。以柴油机进、排气阀故障为例,应用提出的方法进行仿真,仿真结果证明了该方法能够优化神经网络结构,提高故障诊断速度和准确率。
A BP network faults diagnosis method based on the rough set and genetic algorithm is presented, solving problems of low precision, slow constringency and local minimum. In order to simplify the input of BP network, the attributes of training samples are reduced by the rough set theory. Double genetic algorithms are designed to train BP network, for the first time, to optimize the number of hidden layer neurons, secondly to optimize the weights of network. The faults diagnosis for the diesel intake valve and outlet valve is simulated using the proposed method. The results show that the proposed method can optimize the structure of neural network and improve the rate and precision of fault diagnosis.
出处
《控制工程》
CSCD
北大核心
2009年第6期709-712,共4页
Control Engineering of China
基金
国家部委科研基金资助项目(2005615)
航天支撑技术基金资助项目(2008-HT-GFKD)
关键词
粗糙集
神经网络
遗传算法
故障诊断
柴油机
rough set
neural network
genetic algorithm
fault diagnosis
diesel
