Feature Analysis and Recognition of Induced Uranium Components Fission Signal Based on BP Neural Network

XIE Jun-hua; LIU Zhi-gui; REN Li-xue; ZHANG Huo-li

doi:10.11804/NuclPhysRev.29.02.202

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Article Navigation > Nuclear Physics Review > 2012 > 29(2): 202-207

XIE Jun-hua, LIU Zhi-gui, REN Li-xue, ZHANG Huo-li. Feature Analysis and Recognition of Induced Uranium Components Fission Signal Based on BP Neural Network[J]. Nuclear Physics Review, 2012, 29(2): 202-207. doi: 10.11804/NuclPhysRev.29.02.202

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XIE Jun-hua, LIU Zhi-gui, REN Li-xue, ZHANG Huo-li. Feature Analysis and Recognition of Induced Uranium Components Fission Signal Based on BP Neural Network[J]. Nuclear Physics Review, 2012, 29(2): 202-207. doi: 10.11804/NuclPhysRev.29.02.202

Feature Analysis and Recognition of Induced Uranium Components Fission Signal Based on BP Neural Network

doi: 10.11804/NuclPhysRev.29.02.202

Received Date: 1900-01-01
Rev Recd Date: 1900-01-01
Publish Date: 2012-06-20

Abstract

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 principle 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 parameters of induced uranium component fission signal via comparative method and derivative method. UsingBP neural network to recognize patter of fission signal， we got good results that verified the effectiveness and reasonability of the method.
- uranium component,
- BP neural network,
- feature extraction,
- pattern recognition
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通讯作者: 陈斌, bchen63@163.com

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沈阳化工大学材料科学与工程学院沈阳 110142

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Feature Analysis and Recognition of Induced Uranium Components Fission Signal Based on BP Neural Network

doi: 10.11804/NuclPhysRev.29.02.202

Received Date: 1900-01-01
Rev Recd Date: 1900-01-01
Publish Date: 2012-06-20

Key words:

Abstract: 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 principle 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 parameters of induced uranium component fission signal via comparative method and derivative method. UsingBP neural network to recognize patter of fission signal， we got good results that verified the effectiveness and reasonability of the method.

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