Identification of time-varying Wiener systems with unknown parameters

✍ Scribed by Yasuhide Kobayashi; Yuzuru Shiotani; Shinichi Hikita; Kazuya Nomura

• Publisher: Wiley (John Wiley & Sons)
• Year: 2010
• Tongue: English
• Weight: 402 KB
• Volume: 93
• Category: Article
• ISSN: 1942-9533
• DOI: 10.1002/ecj.10247

✦ Synopsis

Abstract

Wiener systems which consist of a dynamic linear block followed by a static nonlinear element have been used in numerous applications. In many cases, the system parameters are affected by changes in the environmental conditions. This paper describes a new approach to the on‐line identification of time‐varying Wiener systems. The time‐varying linear parameters and the static nonlinear characteristics are estimated by neural networks which can represent various nonlinear characteristics. The initial states of the linear model in each estimation window are not available in the Wiener systems. Thus, the initial states and the other system parameters are estimated simultaneously by nonlinear optimization techniques. Furthermore, the optimal numbers of hidden units in the neural networks are determined by the minimum description length (MDL) criterion. As a result of simulation by this method, more accurate parameters can be obtained than without estimation of the initial states and MDL. © 2010 Wiley Periodicals, Inc. Electron Comm Jpn, 93(4): 1–9, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecj.10247