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A hybrid method of modified cat swarm optimization and gradient descent algorithm for training anfis

Orouskhani, M ; Sharif University of Technology | 2013

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  1. Type of Document: Article
  2. DOI: 10.1142/S1469026813500077
  3. Publisher: 2013
  4. Abstract:
  5. This paper introduces a novel approach for tuning the parameters of the adaptive network-based fuzzy inference system (ANFIS). In the commonly used training methods, the antecedent and consequent parameters of ANFIS are trained by gradient-based algorithms and recursive least square method, respectively. In this study, a new swarm-based meta-heuristic optimization algorithm, so-called "Cat Swarm Optimization", is used in order to train the antecedent part parameters and gradient descent algorithm is applied for training the consequent part parameters. Experimental results for prediction of Mackey-Glass model and identification of two nonlinear dynamic systems reveal that the performance of proposed algorithm is much better and it shows quite satisfactory results
  6. Keywords:
  7. Cat swarm optimization ; Prediction and identification ; Adaptive network based fuzzy inference system ; ANFIS ; Gradient based algorithm ; Gradient descent algorithms ; Meta-heuristic optimizations ; Recursive least square methods ; Swarm Intelligence ; Swarm optimization ; Algorithms ; Artificial intelligence ; Nonlinear dynamical systems ; Parameter estimation
  8. Source: International Journal of Computational Intelligence and Applications ; Volume 12, Issue 2 , June , 2013 ; 14690268 (ISSN)
  9. URL: http://www.worldscientific.com/doi/abs/10.1142/S1469026813500077