A new filter‐based stochastic gradient algorithm for dual‐rate ARX models. (12th September 2018)

• Record Type: Journal Article
• Authors: Chen, Jing; Liu, Yanjun; Xu, Ling
• Is Part Of: International journal of adaptive control and signal processing. Volume 32:Number 11(2018)
• Page Start: 1557
• Page End: 1568
• Publication Date: 2018-09-12
• Subjects: auxiliary model -- dual‐rate system -- new filter -- parameter estimation -- stochastic gradient; Adaptive control systems -- Periodicals; Adaptive signal processing -- Periodicals; 629.836
• DOI: 10.1002/acs.2930
• Languages: English
• ISSNs: 0890-6327

Abstract

Summary: This paper proposes a new filter‐based stochastic gradient algorithm for dual‐rate ARX models. Algorithm analysis is based upon the Kalman filter and smoother method. The new filter applies the measurable outputs to adjust the estimated outputs during each interval of the slow sampled rate. A comparative study reveals that the present consideration makes the estimated outputs more accurate than the classical auxiliary model. A stochastic gradient algorithm is developed for the estimation of parameters using all data. The simulation made further guarantees the usefulness of the proposed algorithm.