摘要
针对遗传算法(GA)和粒子群优化(PSO)算法优化支持向量机(SVM)存在容易陷入局部最优解、诊断精度相对较低、鲁棒性较差的问题,提出了一种结合GA、PSO、模拟退火算法的GAPSO优化算法,利用这种算法对SVM的参数进行了优化,优化后的算法能够较好地调整算法的全局与局部搜索能力之间的平衡.通过对航空发动机典型故障的诊断研究表明,该方法不仅能够取得良好的分类效果,诊断精度高于BP神经网络、自组织神经网络、标准SVM、GA-SVM,而且有较好的鲁棒性,更适合在故障诊断中应用.
Genetic algorithm （GA）and particle swarm optimization （PSO）algorithm optimized support vector ma-chine （SVM） has such disadvantages as the tendency to fall into local optimal solution, relatively low diagnostic accu- racy and poor robustness. To solve the problems, an GAPSO algorithm was proposed in this paper, which combines GA, PSC and simulated annealing algorithm together and is used to optimize the parameters of SVM. It is proved that the optimized algorithm can well balance the overall search ability and the local search ability. A typical aircraft engine fault diagnosis shows that the method can achieve good classification effects, with greater diagnostic accuracy than BP neural network, adaptive neural network, the standard SVM and GA-SVM, and it has good robustness. There-fore, it is verified that the proposed algorithm is more suitable for fault diagnosis.
出处
《天津大学学报》
EI
CAS
CSCD
北大核心
2012年第12期1057-1061,共5页
Journal of Tianjin University(Science and Technology)
基金
国家自然科学基金委员会与中国民用航空局联合资助项目(U1233201)
国家自然科学基金资助项目(60879002)
天津市科技支撑计划重点资助项目(10ZCKFGX03800)
关键词
支持向量机
遗传算法
粒子群优化算法
故障诊断
support vector machine
genetic algorithm
particle swarm optimization algorithm
fault diagnosis
