Earticle

초록

영어

This paper proposes a hybrid learning algorithm for tuning an Adaptive Network based Fuzzy Inference System ANFIS. The proposed scheme of adapting parameters in ANFIS employs evolutionary techniques PSO and GA to adjust the antecedent parameters. The least-squares (LSE) algorithm is used to adjust consequent parameters. The number of fuzzy rules is fixed and given by using the Xie Beni’s index. This new approach is applied to identify and control nonlinear systems with an on-line strategy. The obtained results are compared to similar ANFIS using gradient descent method GD as antecedent parameters of training algorithm and other methods applied in the same problems.

목차

Abstract
 1. Introduction
 2. The Concept of ANFIS
  2.1. ANFIS structure
  2.2. Learning algorithm
 3. Meta-heuristic Optimization Methods
  3.1. Genetic Algorithm
  3.2. Particle Swarm Optimization
 4. Proposed Hybrid Algorithms
  4.1. Hybrid PSO - LSE training algorithm
  4.2. Hybrid GA - LSE training algorithm
 5. Simulations
 6. Conclusion
 References

키워드

저자

• Mourad Turki [ Research Unit: Etude des Systèmes Industriels et Energies Renouvelables, The National School of Engineers of Monastir, Tunisia ]
• Sana Bouzaida [ Research Unit: Etude des Systèmes Industriels et Energies Renouvelables, The National School of Engineers of Monastir, Tunisia ]
• Anis Sakly [ Research Unit: Etude des Systèmes Industriels et Energies Renouvelables, The National School of Engineers of Monastir, Tunisia ]
• Faouzi M’Sahli [ Research Unit: Etude des Systèmes Industriels et Energies Renouvelables, The National School of Engineers of Monastir, Tunisia ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605