Application of Support Vector Machine s Parameters Selection in Echo Target Recognition

摘要：

Evolutionary algorithms for selecting support vector machine (SVM) parameter values which are based on genetic algorithm and particle swarm optimization algorithm are researched in this paper, these algorithms have been successfully applied to the real underwater echo target recognition. Experimental comparison and analysis show that the evolutionary algorithms can identify optimal or near optimal parameter settings more efficient and faster than the traditional grid-research. Performance of the evolutionary algorithms is demonstrated on the complex underwater echo target character recognition data set.

关键词： underwater target echo signal recognition SVM parameters selection genetic algorithm particle swarm optimization algorithm

作者: Mu Lin Peng Yuan Zeng Yanyan Lin Zhengqing Zhang Fengzhen

作者单位: Science and Technology on Underwater Test and Control Laboratory Dalian, China

会议类型: 国际会议

会议名称: 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

会议地点: 厦门

会议语种:英文

页码: 369-373

在线出版日期: 2010-10-29（万方平台首次上网日期，不代表论文的发表时间）