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A New Recurrent Neurofuzzy Network for Identification of Dynamic Systems

  • Marcos A. Gonzalez-Olvera
  • Yu Tang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

In this paper a new structure of a recurrent neurofuzzy network is proposed. The network considers two cascade-interconnected Fuzzy Inference Systems (FISs), one recurrent and one static, that model the behaviour of a unknown dynamic system from input-output data. Each FIS’s rule involves a linear system in a controllable canonical form. The training for the recurrent FIS is made by a gradient-based Real-Time Recurrent Learning Algorithm (RTRLA), while the training for the static FIS is based on a simple gradient method. The initial parameter conditions previous to training are obtained by extracting information from a static FISs trained with delayed input-output signals. To demonstrate its effectiveness, the identification of two non-linear dynamic systems is included.

Keywords

Fuzzy System Fuzzy Inference System Recurrent Neural Network Lyapunov Stability Initial Parameter Condition 
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 2006

Authors and Affiliations

  • Marcos A. Gonzalez-Olvera
    • 1
  • Yu Tang
    • 1
  1. 1.Edificio Bernardo Quintana, Engineering FacultyNational Autonomous University of Mexico (UNAM)Mexico CityMexico

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