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Nonlinear Black-Box Modeling in System Identification: a Unified Overview (1995)
| Venue: | Automatica |
| Citations: | 225 - 16 self |
BibTeX
@ARTICLE{Sjöberg95nonlinearblack-box,
author = {Jonas Sjöberg and Qinghua Zhang and Lennart Ljung and Albert Benveniste and Bernard Deylon and Pierre-yves Glorennec and Hakan Hjalmarsson and Anatoli Juditsky},
title = {Nonlinear Black-Box Modeling in System Identification: a Unified Overview},
journal = {Automatica},
year = {1995},
volume = {31},
pages = {1691--1724}
}
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Abstract
A nonlinear black box structure for a dynamical system is a model structure that is prepared to describe virtually any nonlinear dynamics. There has been considerable recent interest in this area with structures based on neural networks, radial basis networks, wavelet networks, hinging hyperplanes, as well as wavelet transform based methods and models based on fuzzy sets and fuzzy rules. This paper describes all these approaches in a common framework, from a user's perspective. It focuses on what are the common features in the different approaches, the choices that have to be made and what considerations are relevant for a successful system identification application of these techniques. It is pointed out that the nonlinear structures can be seen as a concatenation of a mapping from observed data to a regression vector and a nonlinear mapping from the regressor space to the output space. These mappings are discussed separately. The latter mapping is usually formed as a basis function e...
Keyphrases
system identification nonlinear black-box modeling unified overview nonlinear structure model structure wavelet network nonlinear black box structure common feature fuzzy rule output space dynamical system successful system identification application neural network nonlinear dynamic nonlinear mapping regressor space different approach fuzzy set latter mapping considerable recent interest regression vector radial basis network wavelet transform basis function common framework
