Earticle

초록

영어

Artificial rejection system strains of particle swarm optimization algorithm, introduced selection mechanism and nonlinear optimization, using nonlinear mutation operator, such as copying and improve the algorithm convergence speed and population diversity, so as to put forward a kind of based on nonlinear plant choose the rejection of particle swarm optimization algorithm, has advantages of few parameters to adjust, and easy to implement. Combined with Iris classification problem, a weight optimization is applied to the artificial neural network, and the method based on the standard particle group algorithm and the simple comparison of artificial neural network training, the experimental results show that compared with other two kinds of algorithm performance is better, this algorithm has good convergence and stability.

목차

Abstract
 1. Introduction
 2. Related Works
  2.1 Plant Selection Principle
  2.2 . Nonlinear Optimization
  2.3. The Rejection of Particle Group Algorithm based on Nonlinear Strains Fell to Choose
 3. The Rejection of Particle Group Algorithm based on Nonlinear Strains Fell to Choose in the Application of Artificial Neural Network
  3.1. Artificial Neural Network
  3.2. Based on the Nonlinear Strains Fell to Choose the Rejection of Particle Group Artificial Neural Network Learning Algorithm
 4. The Simulation Experiment and Result Analysis
 5. Conclusion
 Acknowledgement
 References

키워드

저자

• Xie Hongsheng [ Shandong Women’s University ]
• Zhao Xiaofeng [ Shandong Women’s University ]

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