Earticle

초록

영어

Due to the insufficient of the fuzzy neural network in solving complex problems, an improved ant colony optimization(ACO) algorithm is introduced into the fuzzy neural network in order to propose a novel hybrid optimization(APEACOFNN) algorithm in this paper. In the APEACOFNN algorithm, the self-adaptive pheromone evaporation factor strategy is used to dynamically adjust the pheromone evaporation factor on searching route in order to gradually lessen the amount of information between the optimal path and the worst path, and realize the full searching optimization for decision variable space. Then an improved ACO(APEACO) algorithm is obtained. Aiming at the parameters optimization problem of fuzzy neural network, the proposed APEACO algorithm is used to comprehensively optimize and select the parameters of fuzzy neural network in order to propose a novel hybrid optimization (APEACOFNN) algorithm. Finally, in order to test the effectiveness of the APEACOFNN algorithm, five UCI data sets are selected. The experimental results show that the proposed APEACOFNN algorithm takes the faster approximation objectives and higher solving accuracy.

목차

Abstract
 1. Introduction
 2. Ant Colony Optimization(ACO) Algorithm and Fuzzy Neural Network
  2.1. Ant Colony Optimization(ACO) Algorithm
  2.2. Fuzzy Neural Network(FNN)
 3. An Improved ACO(APEACO) Algorithm
 4. A Novel Hybrid Optimization (APEACOFNN) Algorithm
  4.1. The Idea of APEACOFNN Algorithm
  4.2. The Steps of APEACOFNN Algorithm
 5. Experiment and Result Analysis
 6. Conclusion
 Acknowledgements
 References[1]

키워드

저자

• Dao Jiang [ School of Electronic and Information Engineering, Shunde Polytechnic, Shunde 528000 China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605