摘要
用HHT变换联合BP人工神经网络分析处理252Cf自发裂变中子源驱动式核信息系统输出的随机核信号,获得信号时频特征,并分类识别核信号样本。理论分析和实验结果表明,基于HHT变换边际谱的特征提取方法,HHT反映信号时频域联合分布能较好地提取实际非平稳随机核信号的时频特征;用BP神经网络分类器对核信号样本分类识别,取得较高正确率,验证了此方法的有效性和合理性。
In this paper, for a nuclear information system driven by a 252Cf spontaneous fission neutron source, the random nuclear signals were analyzed and processed using both Hilbert-Huang Transform （HHT） and artificial neural networks （ANN）. We got its time-frequency features and recognized its nuclear signal samples based on their classification. The results show that the time-frequency feature extraction method based on Hilbert marginal spectrum is applicable to separate the time-frequency feature of non-stationary nuclear random signal because it can reflect the time-frequency distributions. After classified recognition of nuclear signal samples using BP neural network, we also got ideal result that further verified the effectiveness and reasonability of the method.
出处
《核技术》
CAS
CSCD
北大核心
2010年第6期451-456,共6页
Nuclear Techniques
基金
国家军工预研专项基金项目(JW2025067)
重庆市自然科学基金项目(CSTC2009BB2188)资助
关键词
随机核信号
希尔伯特-黄变换
人工神经网络
特征提取
分类识别
Random muclear signal, Hilbert-Huang transform, Artificial neural network, Feature extraction, Classification and recognition
