원문정보
보안공학연구지원센터(IJHIT)
International Journal of Hybrid Information Technology
Vol.9 No.3
2016.03
pp.347-354
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보