Earticle

초록

영어

In allusion to the insufficient of the traditional parameters optimization of the traditional fuzzy neural network PID controller, the parallel search characteristics of the ant colony algorithm in the whole parameter space is used. A parameters optimization method of the PID controller based combining the ant colony algorithm and fuzzy theory and neural network is proposed in this paper. The method used the ant colony algorithm to comprehensively optimize the parameters and structure of fuzzy neural network, which to be used to train and determine the parameters of the PID controller in order to get the fuzzy neural network PID controller. This method is used in the practical application of nonlinear coupled system, the experimental results show that the optimized fuzzy neural network PID controller takes the faster approximation control objectives, the shorter response time, the smaller overshoot and higher control accuracy. Consequently, the research has the theoretical significance and practical application value.

목차

Abstract
 1. Introduction
 2. Artificial Intelligence Technology
  2.1. Ant colony algorithm
  2.2. Fuzzy neural network
 3. The ACO Algorithm Optimizes the Fuzzy Neural Network
  3.1. The idea of optimizing the fuzzy neural network
  3.2. The steps for optimizing the FNN
  3.3. Simulation experiment for optimizing FNN
 4. Application Analysis for Fuzzy Neural Network PID Controller
 5. Conclusions and Future Work
 ACKNOWLEDGEMENTS
 References

키워드

저자

• Chunguang Zhang [ School of Electronics and Information Engineering, Dalian Jiaotong University,Dalian 116028 China ]
• Lifang Zhang [ School of Electronics and Information Engineering, Dalian Jiaotong University,Dalian 116028 China ]

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