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Fuzzy Control pp 287-293 | Cite as

A Neuro-Fuzzy Inference System Optimized by Deterministic Annealing

  • J. Łeski
  • E. Czogała
Part of the Advances in Soft Computing book series (AINSC, volume 6)

Abstract

In this paper artificial neural network based fuzzy inference system (ANNBFIS) learned by deterministic annealing has been described. The system consists of the moving fuzzy consequent in if-then rules. The location of this fuzzy set is determined by a linear combination of system inputs. This system also automatically generates rules from numerical data. The proposed system operates with Gaussian membership functions in premise part. Parameter estimation has been made by connection of both deterministic annealing and least squares methods. For initialization of unknown parameter values of premises, a preliminary fuzzy c-means clustering method has been employed. The application to prediction of chaotic time series is considered in this paper.

Keywords

Fuzzy Rule Chaotic Time Series Gaussian Membership Function Fuzzy Implication Deterministic Annealing 
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 2000

Authors and Affiliations

  • J. Łeski
    • 1
  • E. Czogała
    • 1
  1. 1.Institute of ElectronicsTechnical University of SilesiaGliwicePoland

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