Neural Computing and Applications

, Volume 18, Issue 2, pp 157–174 | Cite as

Identification using ANFIS with intelligent hybrid stable learning algorithm approaches

  • Mahdi Aliyari Shoorehdeli
  • Mohammad Teshnehlab
  • Ali Khaki Sedigh
Original Article

Abstract

This paper suggests novel hybrid learning algorithm with stable learning laws for adaptive network based fuzzy inference system (ANFIS) as a system identifier and studies the stability of this algorithm. The new hybrid learning algorithm is based on particle swarm optimization (PSO) for training the antecedent part and gradient descent (GD) for training the conclusion part. Lyapunov stability theory is used to study the stability of the proposed algorithm. This paper, studies the stability of PSO as an optimizer in training the identifier, for the first time. Stable learning algorithms for the antecedent and consequent parts of fuzzy rules are proposed. Some constraints are obtained and simulation results are given to validate the results. It is shown that instability will not occur for the leaning rate and PSO factors in the presence of constraints. The learning rate can be calculated on-line and will provide an adaptive learning rate for the ANFIS structure. This new learning scheme employs adaptive learning rate that is determined by input–output data.

Keywords

Learning rate Hybrid learning algorithm Intelligent optimization Fuzzy systems TSK system ANFIS Lyapunov theory Identification Stability analysis Fuzzy neural networks Gradient based Recursive least square and particle swarm optimization 

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Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  • Mahdi Aliyari Shoorehdeli
    • 1
  • Mohammad Teshnehlab
    • 1
  • Ali Khaki Sedigh
    • 1
  1. 1.K. N. Toosi University of TechnologyTehranIran

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