电子系统状态时间序列预测的优化相关向量机方法

针对电子系统状态时间序列的预测问题，提出一种基于量子粒子群优化（quantum behaved particle swarm optimization, QPSO）的相关向量机（relevance vector machine, RVM）方法。对电子系统状态时间序列进行相空间重构，建立了RVM回归预测模型；以交叉验证误差最小作为优化目标，将RVM核参数表示为量子空间中的粒子位置，采用QPSO算法实现RVM模型参数的自动优化选择。雷达发射机状态时间序列预测实例表明，相比已有方法，所提方法具有更高的预测精度；同时，能够输出预测值的置信区间，有利于对电子系统未来健康状况做出更加可靠的判断。

Condition time series prediction of electronic system based on optimized relevance vector machin

A method based on optimal relevance vector machine (RVM) is proposed to solve the problem of electronic system condition time series prediction. Based on the phase space reconstruction of electronic system condition time series, the RVM regression model is established. A quantum behaved particle swarm optimization (QPSO) algorithm is employed to realize automatic selection of the established model parameters, which adopts cross validation error as the optimization objective function and takes the kernel parameter as the particle position in quantum space. Experimental results show that the proposed method has higher point prediction accuracy and can provide probabilistic predictions, which is conducive to determine the future health status of electronic systems more reliably.