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A review of training methods of ANFIS for applications in business and economics

Mohd Salleh, Mohd Najib and Hussain, Kashif (2016) A review of training methods of ANFIS for applications in business and economics. International Journal of u- and e- Service, Science and Technology, 9 (7). pp. 165-172. ISSN 20054246

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Abstract

Fuzzy Neural Networks (FNNs) techniques have been effectively used in applications that range from medical to mechanical engineering, to business and economics. Despite of attracting researchers in recent years and outperforming other fuzzy systems, Adaptive Neuro-Fuzzy Inference System (ANFIS) still needs effective parameter training and rule-base optimization methods to perform efficiently when the number of inputs increase. Moreover, the standard gradient based learning via two pass learning algorithm is prone slow and prone to get stuck in local minima. Therefore many researchers have trained ANFIS parameters using metaheuristic algorithms however very few have considered optimizing the ANFIS rule-base. Mostly Particle Swarm Optimization (PSO) and its variants have been applied for training approaches used. Other than that, Genetic Algorithm (GA), Firefly Algorithm (FA), Ant Bee Colony (ABC) optimization methods have been employed for effective training of ANFIS networks when solving various problems in the field of business and finance.

Item Type: Article
Uncontrolled Keywords: ANFIS; fuzzy; metaheuristic; optimization; training
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 30 Sep 2019 03:24
Last Modified: 30 Sep 2019 03:24
URI: http://eprints.uthm.edu.my/id/eprint/11720
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