计算机工程与应用2019，Vol.55Issue(12)：1-5,5.DOI:10.3778/j.issn.1002-8331.1803-0099
PSO优化多核RVM的模拟电路故障预测
Analog Circuit Fault Prognostic Utilizing Particle Swarm Optimized Multi-Kernel Relevance Vector Machine
摘要
Abstract
Aimed at the characteristics of prognostic and health management in analog circuits, a new method for analog circuit fault prediction based on particle swarm optimized multi-kernel RVM is proposed in this paper. Firstly, fault fea-tures are extracted by parameter analysis of the circuit, then the health value of circuit components is represented through calculating the Euclidean distance. Finally, combining different kernel function linearly, the trend of health value trajectory with respect to time points can be predicted by particle swarm optimization, the proposed approach is appropriate for real time prediction in prognostic and health management and is better than the single kernel RVM model in the case of small sample. Simulation results validate the good practicability and effectiveness of the proposed method.关键词
相关向量机/核函数/欧氏距离/模拟电路/粒子群寻优Key words
Relevance Vector Machine(RVM)/ kernel function/ Euclidean distance/ analog circuits/ Particle Swarm Opti-mization(PSO)分类
信息技术与安全科学引用本文复制引用
颜学龙,陈卓..PSO优化多核RVM的模拟电路故障预测[J].计算机工程与应用,2019,55(12):1-5,5.基金项目
广西自动检测技术与仪器重点实验室基金(No.YQ17101). （No.YQ17101）
