CAJ | 학술논문

针对辨识技术应用过程中出现的一类输入数据随机缺失时的辨识问题进行了研究.针对输出误差(OE)模型描述的一类系统,提出了一种模型辨识和缺失数据预测交互迭代的辨识算法.在模型辨识中采用了递推的辨识算法便于形成实时更新的在线辨识策略;而在缺失数据的预测过程中,利用小波降噪技术对预测数据进行适应性的滤波.仿真和分析表明,所提出的辨识算法对连续性输入数据缺失具有很强的鲁棒性;与没有小波技术参与的辨识算法相比,该算法具有较高的模型辨识精度和对缺失数据较好的预测能力.
침대변식기술응용과정중출현적일류수입수거수궤결실시적변식문제진행료연구.침대수출오차(OE)모형묘술적일류계통,제출료일충모형변식화결실수거예측교호질대적변식산법.재모형변식중채용료체추적변식산법편우형성실시경신적재선변식책략;이재결실수거적예측과정중,이용소파강조기술대예측수거진행괄응성적려파.방진화분석표명,소제출적변식산법대련속성수입수거결실구유흔강적로봉성;여몰유소파기술삼여적변식산법상비,해산법구유교고적모형변식정도화대결실수거교호적예측능력.
A research is carried out to deal with the identification using randomly missing input data, which is probably encountered during the application of an identification technique. Aiming at the true system which can be characterized as an output error (OE) model, an interactively iterative identification algorithm consisting of model identification and prediction for missing data, is proposed. During the model identification, a recursive identification algorithm is applied to achieving a real-time and on-line update. In the course of the prediction for missing data, a wavelet de-noising technique is employed to adaptively filter the predicted missing data. Finally, a numerical simulation shows that the proposed algorithm has strong robustness for a segment of missing input data. Compared with the related algorithm without wavelet, the proposed one is capable of giving higher model accuracy and has better prediction ability for missing data.