Model-based neural algorithms for parameter estimation

• Published in: Information sciences Vol. 104; no. 1; pp. 107 - 128
• Main Authors: Skantze, Fredrik P., Annaswamy, Anuradha M.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 1998
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/S0020-0255(97)00077-7

Recently θ-adaptive neural networks (TANN) were developed for parameter estimation for systems with nonlinear parametrization [1]. This paper discusses the estimation problem when additional information is available about the model structure. In particular, algorithms are presented for recursive parameter estimation for systems with partial nonlinear parametrization and for systems where the nonlinearities appear in an additive manner in the regression equation. Training procedures are developed for the neural networks which ensure the stability of the algorithms. It is shown how the training procedures can be modified to ensure stability in the presence of a bounded disturbance. The complexity of the neural networks needed to perform the identification tasks is greatly reduced compared to the TANN algorithm proposed in [1]. Simulation results are presented which demonstrate the capabilities of the algorithms.