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Modeling of Multilinear Dynamical Systems from Experimental Data

  • Jasek Kluska
Chapter
  • 796 Downloads
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 241)

Abstract

Since the introduction of fuzzy sets by Zadeh in 1965 [210], many researchers have shown interest in applying this theory to system identification, which is an essential part of any control system design. Rapid development of intelligent control methodologies such as artificial neural networks, fuzzy logic theory, and rule-based expert systems, have provided alternative tools to tackle the problem of system identification [203]. A large number of fuzzy identification techniques have been developed using neural networks, genetic algorithms, clustering techniques, Kalman filtering and other methods, including ad hoc ones [112]. Consequently, fuzzy identification has become a very important area in fuzzy system theory [180]. The main approaches to fuzzy identification are based on linguistic fuzzy modeling, fuzzy relational equation modeling and Takagi-Sugeno modeling [13]. In this chapter we present a new effective method of modeling continuous multilinear dynamical systems using the Takagi-Sugeno fuzzy expert system.

Keywords

Fuzzy Rule Recursive Little Square Control System Design Recursive Little Square Algorithm Fuzzy Identification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jasek Kluska

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