Earticle

초록

영어

A novel neural network algorithm optimized by particle swarm optimization (PSO) for function approximation is proposed in this paper. The prior information extracted from the upper and lower bound of the approximated function is coupled into PSO. Since the prior information narrows the search space and guides the movement direction of the particles, the convergence rate and the approximation accuracy are improved. Experimental results demonstrate that the new algorithm is more effective than traditional methods.

목차

Abstract
 1. Introduction
 2. Neural Network Optimized by PSO
 3. ULB-PSO-BPNN for Function Approximation
  3.1 PSO-BPNN Coupling with ULB Prior Information
  3.2 The ULB-PSO-BPNN Algorithm
 4. Experimental Results
 5. Conclusions
 Acknowledgement
 References

키워드

저자

• Juanjuan Tu [ School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212003, China ]
• Wenlan Zhou [ School of electronic information, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu212003, China ]
• HongmeiLi [ School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212003, 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