A review of training methods of ANFIS for applications in business and economic
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) stil...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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"Science & Engineering Research Support Society (SERSC) "
2016
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| Online Access: | http://eprints.uthm.edu.my/3384/1/AJ%202016%20%282%29.pdf |
| _version_ | 1825637065063137280 |
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| author | Mohd Salleh, Mohd Najib Hussain, Kashif |
| author_facet | Mohd Salleh, Mohd Najib Hussain, Kashif |
| author_sort | Mohd Salleh, Mohd Najib |
| collection | UTHM |
| description | 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. |
| first_indexed | 2024-03-05T21:45:39Z |
| format | Article |
| id | uthm.eprints-3384 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T21:45:39Z |
| publishDate | 2016 |
| publisher | "Science & Engineering Research Support Society (SERSC) " |
| record_format | dspace |
| spelling | uthm.eprints-33842021-11-17T02:39:27Z http://eprints.uthm.edu.my/3384/ A review of training methods of ANFIS for applications in business and economic Mohd Salleh, Mohd Najib Hussain, Kashif QA76 Computer software 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. "Science & Engineering Research Support Society (SERSC) " 2016 Article PeerReviewed text en http://eprints.uthm.edu.my/3384/1/AJ%202016%20%282%29.pdf Mohd Salleh, Mohd Najib and Hussain, Kashif (2016) A review of training methods of ANFIS for applications in business and economic. A Review Of Training Methods Of Anfis For Applications In Business And Economics, 6 (165). pp. 165-172. ISSN 20054246 htttps://doi.org/10.14257/ijunesst.2016.9.7.17 |
| spellingShingle | QA76 Computer software Mohd Salleh, Mohd Najib Hussain, Kashif A review of training methods of ANFIS for applications in business and economic |
| title | A review of training methods of ANFIS for applications in business and economic |
| title_full | A review of training methods of ANFIS for applications in business and economic |
| title_fullStr | A review of training methods of ANFIS for applications in business and economic |
| title_full_unstemmed | A review of training methods of ANFIS for applications in business and economic |
| title_short | A review of training methods of ANFIS for applications in business and economic |
| title_sort | review of training methods of anfis for applications in business and economic |
| topic | QA76 Computer software |
| url | http://eprints.uthm.edu.my/3384/1/AJ%202016%20%282%29.pdf |
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