RVM analog circuit fault diagnosis based on Fisher criterion function
基于Fisher准则函数的相关向量机模拟电路故障诊断*

Abstract: In order to reducing fuzzy group and overlap of analogous circuit between fault and feature, this article ,first, establishes auto-adapted estimating approach of best cluster number based on Fisher criterion function, fuzzy nuclear cluster is used to select best diagnosable fault component set, and then proposes an analog circuit fault diagnosis model based on relevant vector machine (RVM) from the sparse Bayesian theory, RVM can infer the discriminant function under the Bayesian framework. Moreover, it can obtaining posterior probability of each classification, thus can judge the degree of confidence of classification result, assist diagnosis decision-making. The result indicate that RVM need less RVs than SVs with comparative default accuracy, sparser and generalizing, it is an effective method for analog circuits fault diagnosis.