在对诱发铀部件裂变信号的测量原理及特点分析的基础上,开展了基于BP神经网络的诱发铀部件裂变时间关联信号特征参量分析处理的研究工作。采用无偏估计方法,计算信号的自相关函数和互相关函数,再利用比较法和导数法两种特征量提取方法,提取出不同状态下裂变信号的特征参量,借助于BP神经网络模式识别应用原理进行训练和预测。理论分析和研究结果表明：基于比较法和导数法获得的特征参量能较好地反映诱发铀部件裂变信号的特征;用BP神经网络对裂变信号进行模式识别,取得了较高的正确率,验证了此方法的有效性和合理性。
The paper presents feature parameter analysis and processing in fission time-dependent signal of induced uranium components based on BP-Neural Networks through the analysis of the measuring princi- ple and signal characteristics of induced uranium components fission signal. The auto correlation functions and cross correlation functions are calculated by using unbiased estimate, and then the feature parameters of fission signal in different status are extracted by using feature abstraction method, comparative method and derivative method, and then applied to training and prediction by means of BP-neural networks based on pattern recognition. Theoretical analysis and the results show that, it is effective to obtain feature pa- rameters of induced uranium component fission signal via comparative method and derivative method. Using BP neural network to tiveness and reasonability of recognize patter of fission signal, we got good results that verified the effec the method.
参考文献
| [1] | RELLY D, ENSSLIN N, SMITH H, et al. Passive Nonde- structive Assay of Nuclear Materials ( LA UR 90 732 [R]). USA: LANL, 1991. |
| [2] | 刘成安,伍均.核军备控制核查技术概论.北京:国防工业出版社.2007,26—40. |
| [3] | 郭江,赵晓凤,彭直兴.原子及原子核物理.北京:国防工业出版社.2010,189191. |
| [4] | 王永德,王军.随机信号分析基础.北京:电子工业出版社,2008.26—29. |
| [5] | MIHALCZO J T, MULLENS J A, MATTINGLY J K, et al. NucI Instr and Meth A, 2000, 450: 531. |
| [6] | 魏彪,任勇,唐跃林,等.强激光与粒子束,2010,22(i0):2453. |
| [7] | 王娟,慈林林,姚康泽.计算机工程与科学.2005,25(12):68. |
| [8] | 尚风军,王海霞,周蓉生.物探化探计算技术.2000,22(4):364. |
| [9] | 王永生,孙瑾,王昌金,等.物理学报.2008,57(10):6120. |
- 下载量()
- 访问量()
- 您的评分：
-
10%
-
20%
-
30%
-
40%
-
50%