关键词: 相关向量机, 核函数, 欧氏距离, 模拟电路, 粒子群寻优

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 features 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.

Key words: Relevance Vector Machine（RVM）, kernel function, Euclidean distance, analog circuits, Particle Swarm Optimization（PSO）