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

• Main Authors: Mohd Salleh, Mohd Najib, Hussain, Kashif
• Format: Article
• Language: English
• Published: Science and Engineering Research Support Society (SERSC) 2016
• Subjects: QA76.75-76.765 Computer software
• Online Access: http://eprints.uthm.edu.my/5249/1/AJ%202016%20%2871%29.pdf; http://eprints.uthm.edu.my/5249/; http://dx.doi.org/10.14257/ijunesst.2016.9.7.17

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 rulebase 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.