Training ANFIS structure using simulated annealing algorithm for dynamic systems identification

Haznedar B., Kalınlı A.

NEUROCOMPUTING, vol.302, pp.66-74, 2018 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 302
  • Publication Date: 2018
  • Doi Number: 10.1016/j.neucom.2018.04.006
  • Journal Name: NEUROCOMPUTING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.66-74
  • Keywords: Neuro-fuzzy, ANFIS, Simulated annealing, System identification, PARTICLE SWARM OPTIMIZATION, LIQUID FLOW PATTERNS, NEURAL-NETWORK, FUZZY, PREDICTION
  • Middle East Technical University Affiliated: No

Abstract

In this paper, a new method is presented for the training of the Adaptive Neuro-Fuzzy Inference System (ANFIS). In this work, it is ensured that the best model is created by optimising the premise and consequent parameters of ANFIS by using Simulating Annealing (SA) based on an iterative algorithm. The proposed method was applied to dynamic system identification problems. The simulation results of the proposed method are compared with the Genetic algorithm (GA), Backpropagation (BP) algorithm and different methods from the literature. At the end of this study it was found that the optimisation of ANFIS parameters is more successful by using SA than by GA, BP and the other methods. (C) 2018 Elsevier B.V. All rights reserved.