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Modeling for Dynamic Systems with Fuzzy Sequential Knowledge

  • I. Takeuchi
  • T. Furuhashi
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 59)

Abstract

This paper presents a fuzzy modeling method for dynamic systems, focusing on the knowledge representation framework. The basic idea lies in the use of “fuzzy” sequential knowledge for the description of dynamic characteristics of a system. Symbolic Dynamic System(SDS), a model for symbolic sequences, is extended to deal with “fuzzy” symbolic sequences. This approach introduces topological nature into the symbolic sequences, which allows an interpretation of the knowledge in numerical forms. The model consists of a mixture of sub-models and represents dynamic characteristics by fuzzy transitions among those sub-models. The parameter estimation algorithm, which performs steepest gradient descent in cost functions defined with fuzzy constraints, is presented. Numerical experiments demonstrate the feasibility of the proposed method.

Keywords

Membership Function Dynamic Characteristic Knowledge Representation Target System Convex Polyhedron 
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

© Physica-Verlag Heidelberg 2001

Authors and Affiliations

  • I. Takeuchi
    • 1
  • T. Furuhashi
    • 2
  1. 1.The Institute of Physical and Chemical ResearchNagoya UnivesityJapan
  2. 2.Nagoya UniversityJapan

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