计算机工程2011，Vol.37Issue(18)：7-9,3.
稀疏贝叶斯相关向量机的模拟电路故障诊断
Analogous Circuit Fault Diagnosis on Sparse Bayesian Relevant Vector Machine
摘要
Abstract
Analogous circuit fault diagnosis is influenced by parameter selection of classical machine learning approach, the result of classification relies on parameter whether suitable or not, that is unable to carry on diagnosis online. This paper proposes an analogous circuit fault diagnosis model based on Relevant Vector Machine(RVM) from the sparse Bayesian theory, and improves the weight renewal algorithm. The hypothesis threshold value picks out unrelated weights before they approach infinity, this can reduce the algorithm running time and speed up the weight refresh. RVM can infer the discriminant function under the Bayesian framework. Moreover, it can obtain posterior probability of each classification, thus can judge the degree of classification result confidence, assist diagnosis decision-making. The result indicates that RVM need less relevance vectors than support vector machine with comparative default accuracy, sparser and generalizing. It suits to online fault detection.关键词
相关向量机/稀疏贝叶斯/模拟电路/故障诊断/最大后验概率Key words
Relevant Vector Machine(RVM)/ sparse Bayes/ analogous circuit/ fault diagnosis/ maximum posterior probability分类
信息技术与安全科学引用本文复制引用
杨颖涛,王跃钢,邓卫强,李仁兵..稀疏贝叶斯相关向量机的模拟电路故障诊断[J].计算机工程,2011,37(18):7-9,3.基金项目
国家“973”计划基金资助项日(61355020301) （61355020301）
