Earticle

초록

영어

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.

목차

Abstract
 1. Introduction
 2. The Concept of ANFIS
  2.1. ANFIS Structure
  2.2. ANFIS Learning
 3. Training Methods of ANFIS
 4. ANFIS Applications in Business and Economics
 5. Conclusion
 Acknowledgments
 References

키워드

저자

• Mohd Najib Mohd Salleh [ Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja, Batu Pahat, Johor, Malaysia. ]
• Kashif Hussain [ Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja, Batu Pahat, Johor, Malaysia. ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605