免疫蚂蚁算法优化的RBF网络用于模拟电路故障诊断  

Immune-ant algorithm based RBFNN for fault diagnosis of analog circuits

作　　者：李晴[1] 何怡刚[1] 包伟[1] 

机构地区：[1]湖南大学电气与信息工程学院,长沙410082

出　　处：《仪器仪表学报》2010年第6期1255-1261,共7页Chinese Journal of Scientific Instrument

基　　金：国家杰出青年科学基金(50925727);国家自然科学基金(60876022);国家863计划(2006AA04A104);湖南省科技计划(2008Gk2022);广东省教育部产学研项目(2009B090300196)资助

摘　　要：针对传统蚂蚁算法中出现的易停滞现象,引入免疫机制,提出了一种新的融合算法,即免疫-蚂蚁算法,用其对RBF网络中心参量进行优化,并将优化后的模型用于模拟电路故障诊断。详述了算法原理,构造网络模型的步骤以及故障诊断步骤,并给出了仿真实例。仿真结果表明该方法可以有效克服基本蚂蚁算法的缺陷,优化得到的RBF网络实现了对隐层中心参量的自适应获取,提高泛化能力,用于故障诊断有速度快、准确率高的特点。Aiming at the phenomena of stagnation in traditional ant colony system（AS）,the immune algorithm is introduced,and a new mixed algorithm,i.e.the ant colony system based on immune principle（ACS-IP）,is proposed.The proposed approach was applied to optimize the selection of data center parameters of radial basis function neural network（RBFNN）.And the optimized model was applied to analog circuit fault diagnosis.The algorithm principle,model construction steps and diagnosis steps are described in this paper,and simulation examples are given as well.Simulation results show that this method can effectively overcome the shortcomings in AS;the optimized RBFNN can achieve data acquisition with self-adapting.Also,when applied in diagnosis,the model is more effective and accurate,and has enhanced generalization capability.

关 键 词：蚂蚁算法 免疫算法 RBF网络 模拟电路 故障诊断 

分 类 号：TN711[电子电信—电路与系统]