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
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[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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