A filtering based multi-innovation gradient estimation algorithm and performance analysis for nonlinear dynamical systems. (6th November 2017)

• Record Type: Journal Article
• Authors: Wang, Yanjiao; Ding, Feng
• Is Part Of: IMA journal of applied mathematics. Volume 82:Number 6(2017)
• Page Start: 1171
• Page End: 1191
• Publication Date: 2017-11-06
• Subjects: numerical algorithm -- data filtering -- gradient search -- multi-innovation identification -- dynamical system -- performance analysis; Mathematics -- Periodicals; Mathematics; Periodicals; 519
• DOI: 10.1093/imamat/hxx029
• Languages: English
• ISSNs: 0272-4960

Abstract

Abstract: This article studies the problem for parameter identification of nonlinear dynamical systems (i.e., the Hammerstein–Wiener systems) with additive coloured noises. Based on the gradient search and the key term separation, a generalized extended stochastic gradient (GESG) algorithm is given for estimating the system parameters. To improve the computational efficiency, a data filtering based GESG algorithm and a data filtering based multi-innovation GESG algorithm are derived by applying the data filtering technique and the multi-innovation identification theory. Moreover, the proposed algorithms are proved to be convergent under proper conditions. Finally, the simulation results verify the theoretical analysis.