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Neural Computing and Applications

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01.02.2009 | Original Article

Identification using ANFIS with intelligent hybrid stable learning algorithm approaches

verfasst von: Mahdi Aliyari Shoorehdeli, Mohammad Teshnehlab, Ali Khaki Sedigh

Erschienen in: Neural Computing and Applications | Ausgabe 2/2009

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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.

Vorheriger Artikel Multiscale Bayesian texture segmentation using neural networks and Markov random fields

Nächster Artikel Combining nearest neighbor data description and structural risk minimization for one-class classification

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Titel: Identification using ANFIS with intelligent hybrid stable learning algorithm approaches
verfasst von: Mahdi Aliyari Shoorehdeli
Mohammad Teshnehlab
Ali Khaki Sedigh
Publikationsdatum: 01.02.2009
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 2/2009
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-007-0168-9

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