A novel training algorithm in ANFIS structure

This paper introduces a new hybrid approach for training the adaptive network based fuzzy inference system (ANFIS). The previous works emphasized on gradient base method or least square (LS) based method. In this study we apply one of the swarm intelligent branches, named particle swarm optimization...

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Bibliographic Details
Main Authors: Shoorehdeli, M.A., Teshnehlab, M., Sedigh, A.K.
Format: Conference Proceeding
Language:English
Subjects:
Adaptive systems
Feedforward neural networks
Fuzzy logic
Fuzzy neural networks
Fuzzy systems
Genetic algorithms
Gradient methods
Least squares approximation
Neural networks
Particle swarm optimization
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Summary:This paper introduces a new hybrid approach for training the adaptive network based fuzzy inference system (ANFIS). The previous works emphasized on gradient base method or least square (LS) based method. In this study we apply one of the swarm intelligent branches, named particle swarm optimization (PSO). The hybrid method composes PSO with gradient decent (GD) for training. We use PSO with some changes for training procedure parameters in antecedent part. These changes are inspired from genetic algorithm (GA) method. The simulation results show that in comparison with current GD training, the novel training can have a better adaptation to complex plants. Also, the results show this new hybrid approach optimizes ANFIS parameters faster and better parameters than gradient base method
ISSN:0743-1619
2378-5861
DOI:10.1109/ACC.2006.1657525