摘要
蚁群算法是近几年优化领域中新出现的一种启发式仿生类并行智能进化算法,有正反馈、分布式计算、启发性收敛等特点。本文介绍了蚁群算法的基本原理和算法模型,建立了以电机为对象的神经网络故障诊断系统,应用蚁群算法训练了神经网络并进行了故障诊断,与BP算法的诊断结果进行了比较。网络训练的对比结果表明,基于蚁群算法的神经网络故障诊断系统,对多故障征兆有较好的故障识别率,且算法收敛快,诊断精度高,具有较高的搜索效率。
Ant Colony Algorithrn（ACA） is a new heuristic bionic parallel intelligent evolutionary algorithm in the field of optimization in recent years. It has positive feedback, distributed computation, and heuristic convergence. This paper introduces the principles and the algorithm model of the ant colony algorithm. Then the fault diagnosis system of neural network（NN）is established. ACA algorithm is used to train a NN for fault diagnosis of motor. The diagnostic results based on ACA are compared with ones of BP algorithm, contrast results of Network training show that fault diagnosis system based on ACA algorithm has a good identification probability of faults for multi-fault symptoms. ACA algorithm has faster convergence rate, higher accuracy and higher searching efficiency.
出处
《大电机技术》
北大核心
2009年第1期26-30,共5页
Large Electric Machine and Hydraulic Turbine
关键词
蚁群算法
神经网络
故障诊断
Ant Colony Algorithm（ACA）
neural network
fault diagnosis
