Extended fuzzy function model with stable learning methods for online system identification
International Journal of Adaptive Control and Signal Processing, vol.25, no.2, pp.168-182, 2011 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 25 Issue: 2
- Publication Date: 2011
- Doi Number: 10.1002/acs.1214
- Journal Name: International Journal of Adaptive Control and Signal Processing
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Page Numbers: pp.168-182
- Keywords: adaptive learning rate, extended fuzzy function model, input-to-state stability, online system identification
- Yıldız Technical University Affiliated: No
Abstract
The aim of the online nonlinear system identification is the accurate modeling of the current local input-output behavior of the plant without using any prior knowledge and offline modeling phase. It is a challenging task for many intelligent systems when used for real-time control applications. In this paper, we propose a novel computationally efficient extended fuzzy functions (EFF) model for system identification of unknown nonlinear discrete-time systems. The main contributions are to introduce an effective quasi-nonlinear model (EFF) and propose adaptive learning rates (ALR) for recursive least squares (RLS) and gradient-descent (GD) methods. The asymptotic convergence of the modeling errors and boundedness of the parameters are proved by using the input-to-state stability (ISS) approach. Numerical simulations are performed for Box-Jenkins gas furnace system and a nonlinear dynamic system. The benefits of its accuracy, stability and simple implementation in practice indicate that EFF model is a promising technique for online identification of nonlinear systems. © 2010 John Wiley & Sons, Ltd.