Earticle

초록

영어

The core issues of RBF network design are to design the minimum structure neural networks that can meet the accuracy requirements, in order to ensure the generalization ability of the network. For the purpose of simplifying the structure of RBF network, proposes a learning method of RBF network based on improved particle swarm. The method automatically constructs frugal structure of RBF network model by the combining algorithm of regularized least squares method and D- optimal experimental design; chooses three learning parameters of the combining algorithm that can affect network generalization performance by the improved particle swarm optimization algorithm. By nonlinear time series modeling, verifies the effectiveness of the method in this paper.

목차

Abstract
 1. Introduction
 2. Combined with the Regularized Orthogonal Least Squares Method and D- Optimal Experiment Design Method
 3. IPSO
 4. The Improved PSO RBF Method
  4.1. The Basic Idea of RBF Network Design Method
  4.2. Improved RBF Neural Network PSO Optimal Learning Method Based on Adaptive Selection Function
 5. Experimental Examples
 6. Conclusion
 References

키워드

저자

• Ji Weidong [ College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, China / Computer science and Information Engineering College, Harbin Normal University, Harbin, China ]
• Sun Liping [ College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, China ]
• Wang Keqi [ College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, China ]
• Lv Liguo [ Computer science and Information Engineering College, Harbin Normal University, Harbin, China ]
• Li Yue [ Computer science and Information Engineering College, Harbin Normal University, Harbin, 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