自适应建模相关向量机及其在电子系统状态预测中的应用  

Adaptive modeling relevance vector machine and its application in electronic system state prediction

作　　者：高明哲[1] 许爱强[1] 张伟[1] 

机构地区：[1]海军航空工程学院科研部,山东烟台264001

出　　处：《系统工程与电子技术》2017年第8期1898-1904,共7页Systems Engineering and Electronics

基　　金：装备预研基金资助课题

摘　　要：提出一种自适应建模相关向量机(adaptive modeling relevance vector machine,AM-RVM)并将其用于电子系统的状态预测。相比传统RVM,所提方法首先在预测模型训练之前通过计算各类基函数的后验概率来选取最适合训练样本结构特点的基函数,然后在训练中采用优化的增量学习流程来实现各核参数的快速自适应选取,最后通过对电子系统状态参量的相空间重构,从而将AM-RVM应用到电子系统的状态预测中。混沌时间序列预测及雷达发射机高压电源状态预测实验的结果表明,所提方法在预测精度与训练效率上优于传统RVM。An adaptive modeling relevance vector machine （AM -RVM ） algorithm is proposed and used for state prediction of electronic systems. Compared with traditional RVM,the proposed algorithm can select the most suitable basis function for the training data through posterior probability values before training. Then the kernel parameters are selected fast and automatically through an optimized incremental learning process. Final-ly, AM-RVM is used for state prediction of electronic systems after phase space reconstruction of the state pa-rameters of the electronic system. Experimental results of chaotic time series prediction and radar transmitter state prediction indicate that AM-RVM outperforms the traditional algorithm in both prediction accuracy and training speed.

关 键 词：相关向量机 自适应建模 电子系统 状态预测 

分 类 号：TP206[自动化与计算机技术—检测技术与自动化装置]