Parallel type genetic Elman neural network-based source driving 235U concentration recognition method

A neural network and neural network model technology, applied in the field of source-driven 235U concentration recognition, can solve the problem of low recognition accuracy, achieve high recognition accuracy, avoid single neural network structure, and achieve accurate recognition effects

Inactive Publication Date: 2010-06-02
CHONGQING UNIV
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Problems solved by technology

[0006] The problem to be solved by the present invention is how to effectively improve the information utilization rate of the autocorrelation function of the neutron pulse signal, and overcome the shortcomings of the existing method that simply adopts the integral method to cause low recognition accuracy

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  • Parallel type genetic Elman neural network-based source driving 235U concentration recognition method
  • Parallel type genetic Elman neural network-based source driving 235U concentration recognition method
  • Parallel type genetic Elman neural network-based source driving 235U concentration recognition method

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Embodiment Construction

[0051] The present invention will be further elaborated below in conjunction with the accompanying drawings and examples. It should be emphasized that the following description is only exemplary and not intended to limit the scope of the invention and its application.

[0052] This example will come from 235 U concentrations were 80.09%, 84.97%, 90.03%, 93.15% (expressed as 0.8009, 0.8497, 0.9003, 0.9315 in decimal form, concentration grade G=4) obtained by the second channel (that is, determined The second channel signal is the use signal) each ten groups of neutron pulse signal autocorrelation function time series (that is, the number of times S=10 for each concentration nuclear material measurement), a total of 40 groups of samples are used as training samples (see Figure 5 , the sample number is in accordance with 235 U concentrations are arranged in four groups from low to high, and the following are analyzed with the second channel autocorrelation function); the nucle...

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Abstract

The invention discloses a parallel type genetic Elman neural network-based source driving 235U concentration recognition method, which mainly comprises the following steps: establishing a neural network model, wherein the neural network model is structurally divided into three layers which are a data allocation layer, a sub-network layer and a comprehensive decision-making layer respectively; preprocessing an acquired neutron pulse signal auto-correlation function; inputting a processed signal auto-correlation function sample into the data allocation layer of a parallel type genetic Elman network and adopting a cyclic random multi-point sampling method to allocate the data of the sample; inputting the allocated data into each genetic Elman sub-network in the sub-network layer respectivelyfor recognition and giving recognition results; and performing comprehensive processing on the recognition results of the plurality of sub-networks by the comprehensive decision-making layer to obtain a final 235U concentration recognition result. Through the method, relatively better 235U concentration recognition effect is obtained due to relatively higher data utilization ratio and a novel network structure.

Description

technical field [0001] The invention belongs to the technical field of neural network and verification, and relates to a source-driven method based on a parallel genetic Elman neural network. 235 U concentration identification method. Background technique [0002] The NMIS / NWIS (nuclear material / nuclear weapon identification) system in the field of verification technology, that is, the verification system, aims to measure the characteristic parameters of nuclear materials to infer their uses. One of its important functions is to detect the concentration of nuclear materials ( As we all know, the enrichment degree of nuclear materials is the basis for identifying whether it is civilian grade or weapon grade, and it is also one of the signs to judge the level of its nuclear industry). From the perspective of the way to obtain the radiation signal of nuclear materials, the technical route of verification can be divided into two types: passive and active. In view of the low ra...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N33/00G01T3/00G06N3/02G06N3/08G06N3/12
Inventor 金晶魏彪冯鹏任勇周密
Owner CHONGQING UNIV
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