CAJ | 학술논문

在相关向量机回归模型的基础上，提出了一种新的氢气传感器故障数据恢复方法。利用小生境粒子群算法的“共享机制”对相关向量回归的核参数进行了优化，使其能快速准确地找到全局最优参数。用优化并训练后的回归模型对发生故障后的数据进行预测，实现故障恢复。将本文所用方法与其他较成熟的方法进行了比较，实验结果表明本方法在恢复准确度和鲁棒性方面均优于传统方法。数据恢复相对误差在±2.8％以内。
재상관향량궤회귀모형적기출상，제출료일충신적경기전감기고장수거회복방법。이용소생경입자군산법적“공향궤제”대상관향량회귀적핵삼수진행료우화，사기능쾌속준학지조도전국최우삼수。용우화병훈련후적회귀모형대발생고장후적수거진행예측，실현고장회복。장본문소용방법여기타교성숙적방법진행료비교，실험결과표명본방법재회복준학도화로봉성방면균우우전통방법。수거회복상대오차재±2.8％이내。
Based on relevance vector machine regression model ,a new fault data recovery method of hydrogen sensor was pro-posed in this paper .The “sharing mechanism” of niche particle swarm optimization algorithm was used to optimize kernel parame-ter of RVM,which can make it to find the global optimal parameter fast and exactly .The fault data were prognosed by using the re-gression model of optimized and trained to realize the fault recovery .The proposed method was compared with other mature algo-rithms ,the results show that the proposed method is superior to the traditional ones in recovery accuracy and robustness .The relative error of fault recovery is within ±2.8%.