An Improved Quantum-behaved Particle Swarm Classifier Based on Weighted Mean Best Position

摘要：

Aiming at the weaknesses of PS-classifier, it is easily trapped into locally optimal solution and slow convergence velocity when it deals with the complex problems, an improved Quantum-behaved particle swarm classifier has been proposed in the paper. Firstly, It introduce the weighted mean best position to improve the performance of QPSO (Quantum-behaved particle swarm), and use a novel Michigan rule to code speech parameters. Then, a new fitness function is constructed to accomplish the weighted Quantumbehaved particle swarm classifier (WQPS -classifier). Finally it was applied into speaker recognition. Experimental results show that the proposed classifier achieve higher recognition rate in noisy environments compared with other classification algorithms.

关键词： QPSO Speaker recognition WQPS-classifier Pattern classification

作者: Rui Li Wei-juan Li Lin Zhang Ming Li

作者单位: School of Computer and Communication Lanzhou University of Technology LanZhou,China

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(2009 IEEE 智能计算与智能系统国际会议)

会议地点: 上海

会议语种:英文

页码: 2856-2860

在线出版日期: 2009-11-20（万方平台首次上网日期，不代表论文的发表时间）