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Artificial Intelligence Review

Erschienen in:

03.01.2018

Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey

verfasst von: Dervis Karaboga, Ebubekir Kaya

Erschienen in: Artificial Intelligence Review | Ausgabe 4/2019

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Abstract

In the structure of ANFIS, there are two different parameter groups: premise and consequence. Training ANFIS means determination of these parameters using an optimization algorithm. In the first ANFIS model developed by Jang, a hybrid learning approach was proposed for training. In this approach, while premise parameters are determined by using gradient descent (GD), consequence parameters are found out with least squares estimation (LSE) method. Since ANFIS has been developed, it is used in modelling and identification of numerous systems and successful results have been achieved. The selection of optimization method utilized in training is very important to get effective results with ANFIS. It is seen that derivate based (GD, LSE etc.) and non-derivative based (heuristic algorithms such us GA, PSO, ABC etc.) algorithms are used in ANFIS training. Nevertheless, it has been observed that there is a trend toward heuristic based ANFIS training algorithms for better performance recently. At the same time, it seems to be proposed in derivative and heuristic based hybrid algorithms. Within the scope of this study, the heuristic and hybrid approaches utilized in ANFIS training are examined in order to guide researchers in their study. In addition, the final status in ANFIS training is evaluated and it is aimed to shed light on further studies related to ANFIS training.

Vorheriger Artikel Character and numeral recognition for non-Indic and Indic scripts: a survey

Nächster Artikel Application of artificial intelligence techniques in the petroleum industry: a review

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Titel: Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey
verfasst von: Dervis Karaboga
Ebubekir Kaya
Publikationsdatum: 03.01.2018
Verlag: Springer Netherlands
Erschienen in: Artificial Intelligence Review / Ausgabe 4/2019
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-017-9610-2

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