摘要
针对支持向量机(SVM)分类模型参数选取困难的问题,提出基于遗传免疫的改进粒子群优化算法,克服传统粒子群算法前期收敛快、后期易陷入局部最优的缺陷。将该算法与优化支持向量机分类模型相结合,建立基于遗传免疫粒子群和支持向量机的诊断模型,并用于轴承故障诊断中。结果表明,基于遗传免疫粒子群算法优化的SVM可实现对SVM分类模型参数的自动优化,并能提高SVM分类模型的故障诊断精度,对分散程度较大、聚类性较差的故障样本分类有较强的适用性。
In order to resolve the difficulty that the choice of parameters influence the accuracy of Support Vector Machine（SVM） fault diagnosis model, a genetic-immune Particle Swarm Optimization（PSO） algorithm based on genetic evolution algorithm and immune selection algorithm is presented and used to optimize model parameters of SVM. The forecasting model based on a genetic-immune PSO algorithm and SVM is proposed and used to diagnose bearing fault. The results show that diagnosis model of SVM optimized by genetic-immune PSO algorithm can achieve automatic optimization of parameters, increase diagnosis accuracy of the conventional cross-validation algorithm, and is more fitting to classify the faulty samples scattered greatly.
出处
《计算机工程》
CAS
CSCD
2013年第3期187-190,196,共5页
Computer Engineering
基金
中央高校基本科研业务费专项基金资助项目(DL11BB32)
黑龙江省科技厅自然科学基金资助项目(F201028)
关键词
支持向量机
故障诊断
粒子群优化
遗传免疫
轴承
交叉验证
Support Vector Machine（SVM）
fault diagnosis
Particle Swarm Optimization（PSO）
genetic-immune
bearing
cross-validation
