基于输出误差模型的不规则损失输出数据系统递推最小二乘参数估计.pdf

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING Int. J. Adapt. Control Signal Process. 2010; 24:540–553 Published online 18 August 2009 in Wiley InterScience (). DOI: 10.1002/acs.1141 Least-squares parameter estimation for systems with irregularly missing data Feng Ding∗, † and Jie Ding School of Communication and Control Engineering, Jiangnan University , Wuxi 214122, People’s Republic of China SUMMARY This paper considers the problems of parameter identification and output estimation with possibly irregularly missing output data, using output error models. By means of an auxiliary model (or reference model) approach, we present a recursive least-squares algorithm to estimate the parameters of missing data systems, and establish convergence properties for the parameter and missing output estimation in the stochastic framework. The basic idea is to replace the unmeasurable inner variables with the output of an auxiliary model. Finally, we test the effectiveness of the algorithm with an example system. Copyright q 2009 John Wiley Sons, Ltd. Received 28 September 2008; Revised 18 April 2009; Accepted 10 July 2009 KEY WORDS: signal processing; parameter estimation; missing data; identification; output estimation; multirate systems; least squares; convergence properties; irregular observations; auxiliary models 1. INTRODUCTION example, infrequent/scarce output measurement due to sensory limitation [2], irregularly sampled systems Many least-squares-based identification methods can [3], and unexpected inte