Earticle

초록

영어

A novel chaotic-neuron model is presented by introducing the non-monotonous activation function which is composed of the Legendre function and the Sigmoid function. The reversed bifurcation of the chaotic neuron model is given and analyzed, meanwhile, how do parameters influence the network convergence speed is discussed. Based on the neuron model, the piecewise simulated annealing SLF chaotic neural network was made by introducing the simulated annealing idea, the model improve the convergence speed, at the same time, the precision of this network have not being influenced. The simulation experiment of function optimization and TSP problem verify the effectiveness of the segmented simulated annealing strategy.

목차

Abstract
 1. Introduction
 2. SLF Chaotic Neuron Model and the Impact of Parameter
  2.1. SLF Chaotic Neurons Model
  2.2. Effects of Combination Parameters on the Convergence Rate
  2.3. Effects of Annealing Parameters on the Convergence Rate
 3. SLF Chaonic Network Model with Piecewise Simulated Annealing Strategy
 4. The Applications of SLF Chaotic Neural Network with Piecewise Simulated Annealing Strategy
  4.1. Application of Model in Function Optimization
  4.2. Application of Model in Combination Optimization (TSP)
 5. Conclusion
 References

키워드

저자

• Yonggang Ye [ School of Basic Science, Harbin University of Commerce Harbin Heilongjiang China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605