基于支持向量机的^(252)Cf中子裂变信号时频特征分析及识别  

Time-frequency feature analysis and recognition of fission neutrons signal based on support vector machine

作　　者：金晶[1] 魏彪[1] 冯鹏[1] 唐跃林[1] 周密[1] 

机构地区：[1]重庆大学光电工程学院光电技术及系统教育部重点实验室,重庆400044

出　　处：《强激光与粒子束》2010年第10期2441-2447,共7页High Power Laser and Particle Beams

基　　金：国防科技基础研究基金项目;重庆市自然科学基金项目(CSTC2009BB2188)

摘　　要：基于裂变中子(252Cf)对裂变链(235U系统)依存关系,在对252Cf中子裂变信号的测量原理及信号特点分析基础上,开展了基于支持向量机的中子裂变信号时频特征分析及识别研究工作。采用小波分解和去噪小波包分解方法,提取不同状态下随机核信号的时频能量特征,借助于统计学习理论的支持向量机(SVM)分类器原理进行训练和分类。研究结果表明:通过直接小波分解或去噪小波包分解,以获取核信号特征的方法是有效的;去噪小波包分解特征提取方式,较之直接小波分解特征提取方式更能反映中子裂变核系统的内部特征和规律;基于SVM核信号样本的分类,训练后的SVM分类器有着大于70%以上的正确率,且较好地克服了训练样本数较少的问题,验证了方法的可行性和有效性。Based on the interdependent relationship between fission neutrons（235Cf） and fission chain（235U system）, the paper presents the time-frequency feature analysis and recognition in fission neutron signal based on support vector machine（SVM） through the analysis on signal characteristics and the measuring principle of the 235Cf fission neutron signal. The time frequency characteristics and energy features of the fission neutron signal are extracted by using wavelet decomposition and de-noising wavelet packet decomposition, and then applied to training and classification by means of support vector machine based on statistical learning theory. The results show that, it is effective to obtain features of nuclear signal via wavelet decomposition and de-noising wavelet packet decomposition, and the latter can reflect the internal characteristics of the fission neutron system better. With the training accomplished, the SVM classifier achieves an accuracy rate above 70%, overcoming the lack of training samples, and vet ifying the effectiveness of the algorithm.

关 键 词：裂变中子源 小波包分解 特征提取 支持向量机 分类识别 

分 类 号：TL375.1[核科学技术—核技术及应用]