Novel Hybrid Learning Algorithms for Tuning ANFIS Parameters Using Adaptive Weighted PSO

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: Aliyari Shoorehdeli, M., Teshnehlab, M., Sedigh, A.K.
Format: Conference Proceeding
Language:English
Subjects:
Adaptive systems
Adaptive Weighted Particle Swarm
ANFIS
Fuzzy logic
Fuzzy neural networks
Fuzzy systems
Genetic algorithms
Identification
Least squares approximation
Least squares methods
Neural networks
Neuro- Fuzzy
Nonlinear dynamical systems
Optimization
Particle swarm optimization
Recursive Least Square
Swarm Intelligent
TSK System
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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 recursive least square (RLS) for training. We use PSO with some changes for training procedure parameters in antecedent part. These changes are inspired from Genetic Algorithm (GA) method and using Adaptive Weighted for PSO. The simulation results show that in comparison with current gradient based training, the novel training can have a comparable adaptation to complex plants and train less parameter than gradient base methods. Also, the results show this new hybrid approach has less complexity than other gradient based methods.
ISSN:1098-7584
DOI:10.1109/FUZZY.2007.4295571