CN112733439A - Method for calculating shielding material accumulation factor based on BP neural network - Google Patents
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Abstract
The invention discloses a method for calculating an accumulation factor of a shielding material based on a BP neural network. The invention relates to the technical field of radiation shielding calculation for determinism; carrying out forward propagation, wherein the forward ship process is that an input signal is transmitted to an output layer from an input layer through a hidden layer neuron, an output signal is generated at an output end, and when the output signal meets a given output requirement, the calculation is finished; when the output signal does not meet the given output requirement, the signal is shifted to the reverse propagation. The invention divides the data into a training set and a testing set according to a reasonable proportion, and carries out normalization processing on the data. The network is trained using a training set and validated using a test set. The optimal hidden layer and number of neurons per layer can be determined by trial and error.
Description
Technical Field
The invention relates to the technical field of radiation shielding calculation for determinism, in particular to a method for calculating an accumulation factor of a shielding material based on a BP neural network.
Background
The point-kernel integration method is a method for simplifying the calculation of radiation dose field distribution in three-dimensional space. Point-kernel integration simplifies the radiation dose field distribution calculation in a complex three-dimensional space into two parts, the first part is the calculation of the direct-through term which does not interact with the shielding material, and the second part is the calculation of the accumulation factor of the interaction between the particles and the material. For the calculation of the accumulation factor, the fitting taylor formula and G-P formula are mainly used in the conventional calculation method. For the fitting formula, the expression is complex, and the calculation parameters need to be subjected to table lookup for linear interpolation calculation in practical application, so that certain errors are brought to the calculation of the accumulation factors.
Disclosure of Invention
The invention provides a method for training the accumulation factors in the ANSI6.4.3 standard database by adopting a BP neural network to obtain a neural network model for calculating and predicting the accumulation factors under different energies and different mean free paths. The model can calculate the accumulation factor of the material by only inputting two parameters of energy (E) and mean free path (mfp), thereby not only reducing the complicated parameter input and calculation, but also greatly improving the calculation precision and being more beneficial to programming. The invention provides a method for calculating an accumulation factor of a shielding material based on a BP neural network, which provides the following technical scheme:
a method for calculating shielding material accumulation factor based on BP neural network carries out forward propagation, the forward propagation process is that input signals are transmitted to an output layer from an input layer through hidden layer neurons, output signals are generated at an output end, and when the output signals meet given output requirements, calculation is finished; when the output signal does not meet the given output requirement, the signal is shifted to the reverse propagation.
Preferably, the forward propagation of a neuron in the neural network is represented by:
wherein,
representing the input of the ith neuron of the jth layer, and when j is the first layer, the input parameters are gamma ray energy E and mean free radicalStage mfp;
represents the input and weight w of the ith neuron of the j layeriThe sum of the products of (a) and (b) plus an offsetj(ii) a (z) represents an activation function,
representing the output of the j-th layer and simultaneously being used as the input of the j + 1-th layer, when the j-th layer is the output layer
For the final signal output value, when the network output value is not equal to the actual value, there is a loss value L, which is expressed by the following equation:
where y' is the expected value of the neural network output and y is the actual value.
Preferably, the neural network model suggests using relu activation functions, i.e., σ (z) ═ max { z,0}, and other activation functions may also implement the method, and sigmoid and tanh activation functions may also be used.
Preferably, the BP training algorithm employed optimizes the weights by attempting to minimize the sum of squared differences between the expected and actual values of the output neurons, the computation ending when L < ═ e, where e is the desired accuracy of the computation, or going on to a pre-set number of learning times, otherwise the back propagation computation is done.
Preferably, the BP algorithm calculates the weight w and the offset b so that L (w, b) is minimized.
Preferably, the principle of adjusting the weight value is to make the error decrease continuously, so that the adjustment should be performed along the negative gradient direction of the weight value, and the adjustment amount of the weight value is proportional to the decrease of the gradient of the error by the following formula:
wherein, alpha represents the learning rate and is a given constant, 0 < alpha < 1;
representing the partial derivative of the loss function L to the ith input weight of the jth layer;
representing the partial derivative of the loss function L to the offset b of the j layer;
iteratively updating the neuron connection weight and the offset used for the next round of network learning and training according to the obtained change increment of the neuron connection weight and the offset of each layer, and updating by the following formula:
bj＝bj+Δbj
after solving new weight and offset of each layer, carrying out iterative calculation in the forward propagation process of steering;
the above is to complete a BP iteration loop, and when the calculated loss value L < ═ e is the set desired precision, and the smaller the setting, the higher the learning precision, the whole machine learning process is completed.
The invention has the following beneficial effects:
the invention divides the data into a training set and a testing set according to a reasonable proportion, and carries out normalization processing on the data. The network is trained using a training set and validated using a test set. The optimal hidden layer and number of neurons per layer can be determined by trial and error. In the neural network model provided by the algorithm, the activation functions of the hidden layer and the output layer are recommended to be relu functions, and the loss value L is recommended to be set to be less than 1e-4, so that the precision is fully improved. In addition, the learning rate is also crucial to the configuration of the BP neural network, and in the algorithm, the initial learning rate is set to 0.001, and the learning rate is gradually reduced in the training process.
Drawings
FIG. 1 is a schematic diagram of a neural network architecture;
FIG. 2 is a flow chart of a method for calculating a shielding material accumulation factor based on a BP neural network;
FIG. 3 is a diagram illustrating the variation of mean square error with training times;
FIG. 4 is a graph comparing data prediction and results.
Detailed Description
The present invention will be described in detail with reference to specific examples.
The first embodiment is as follows:
according to fig. 1 to 4, the invention provides a method for calculating a shielding material accumulation factor based on a BP neural network, which comprises the following steps:
the method provides that a neural network algorithm is used for realizing the rapid and accurate calculation of the accumulation factor of the material, an ANSI6.4.3 standard database is used as basic training data to train a neural network model, and the input parameters are x respectively1(gamma ray energy E), x2(mean free path mfp) penetration depth of up to 40mfp can be calculated. The computation process includes two parts, forward propagation computation and backward propagation computation, respectively.
In the forward propagation process, an input signal is transmitted from an input layer to an output layer through a hidden layer neuron, an output signal is generated at an output end, and if the output signal meets a given output requirement, the calculation is finished; if the output signal does not meet the given output requirement, the signal is shifted to the reverse propagation. The forward propagation of a neuron in a neural network is calculated as follows:
in the formula,
representing the input of the ith neuron of the jth layer, if j is the first layer, the input parameters are gamma ray energy E and mean free path mfp;
represents the input and weight w of the ith neuron of the j layeriThe sum of the products of (a) and (b) plus an offsetj(ii) a f (z) represents an activation function, commonly used activation functions are sigmoid, tanh, relu and the like, and the neural network model proposed by the algorithm suggests adopting a relu activation function, namely sigma (z) max { z,0 }.
Represents the output of the j-th layer and also serves as the input of the j + 1-th layer. If the jth layer is an output layer, then
Outputting the value for the final signal. When the network output value is not equal to the actual value, there is a loss value L, and the loss function is defined as follows:
where y' is the expected value of the neural network output and y is the actual value. The BP training algorithm employed in the present algorithm optimizes the weights by attempting to minimize the sum of squared differences between the expected and actual values of the output neurons. When L < ═ e (e is the desired accuracy of calculation) or the preset learning times are reached, the calculation is ended, otherwise, the back propagation calculation is carried out.
The purpose of the BP algorithm is to compute the weights w and offsets b to minimize L (w, b). The principle of adjusting the weight is to make the error decrease continuously, so the adjustment should be performed along the negative gradient direction of the weight, that is, the adjustment amount of the weight is proportional to the gradient decrease of the error, that is:
wherein, alpha represents the learning rate and is a given constant, and is usually 0 < alpha < 1;
representing the partial derivative of the loss function L to the ith input weight of the jth layer;
representing the partial derivative of the penalty function L to the layer j offset b. Iteratively updating the neuron connection weight and the offset used for the next round of network learning and training according to the obtained change increment of the neuron connection weight and the offset of each layer, wherein the updating formula is as follows:
bj＝bj+Δbj
and after new weight values and offset values of each layer are solved, carrying out iterative calculation in a forward propagation process. The above description is that a BP iteration loop is completed, and when the calculated loss value L < ═ e (e is the set desired precision, and the smaller the value is, the higher the learning precision is), the whole machine learning process is completed.
The data is divided into a training set and a test set according to the ratio of 8:2, and the data is normalized. The network is trained using a training set and validated using a test set. The optimal hidden layer and number of neurons per layer can be determined by trial and error. In the neural network model provided by the algorithm, the activation functions of the hidden layer and the output layer are recommended to be relu functions, and the loss value L is recommended to be set to be less than 1e-4, so that the precision is fully improved. In addition, the learning rate is also important for the configuration of the BP neural network, and in the algorithm, the initial learning rate is set to be 0.001, and the learning rate is gradually reduced in the training process.
Taking the calculation of the iron accumulation factor as an example:
after training, the data of the training set and the data of the test set both converge, and the mean square error calculated by the training set and the test set is less than 0.5. And selecting gamma-ray energy of 1MeV, and verifying the calculation result, wherein the verification result is shown in the following tables 1 and 2.
TABLE 1 iron accumulation factor (E. 1MeV)
| E＝1MeV | Raw data | Calculating data | Relative error |
| mfp＝0.5 | 1.53 | 1.5126 | -1.1373％ |
| mfp＝1.0 | 2.14 | 2.1279 | -0.5654％ |
| mfp＝2.0 | 3.50 | 3.4700 | -0.8571％ |
| mfp＝3.0 | 5.04 | 5.0388 | -0.0238％ |
| mfp＝4.0 | 6.79 | 6.7649 | -0.3697％ |
| mfp＝5.0 | 8.74 | 8.7235 | -0.1888％ |
| mfp＝6.0 | 10.90 | 10.8850 | -0.1376％ |
| mfp＝7.0 | 13.20 | 13.1871 | -0.0977％ |
| mfp＝8.0 | 15.70 | 15.6891 | -0.0694％ |
| mfp＝10.0 | 21.10 | 21.0803 | -0.0934％ |
| mfp＝15.0 | 37.10 | 37.0874 | -0.0340％ |
| mfp＝20.0 | 56.20 | 56.1902 | -0.0174％ |
| mfp＝25.0 | 77.90 | 77.8861 | -0.0178％ |
| mfp＝30.0 | 102.00 | 102.0074 | 0.0073％ |
| mfp＝35.0 | 128.00 | 127.9324 | -0.0528％ |
| mfp＝40.0 | 156.00 | 156.0267 | 0.0171％ |
Table 2 data prediction vs. results (Fe, E ═ 1MeV)
| mfp | BP neural network | G-P formula | Taylor formula | MCNP |
| 0.5 | 1.51265 | 1.52806 | 1.41874 | 1.57773 |
| 1 | 2.12789 | 2.13000 | 1.85522 | 2.20260 |
| 1.5 | 2.77177 | 2.78954 | 2.31006 | 2.86442 |
| 2 | 3.46995 | 3.50194 | 2.78388 | 3.47787 |
| 2.5 | 4.13720 | 4.26462 | 3.27732 | 4.35071 |
| 3 | 5.03882 | 5.07588 | 3.79105 | 5.16554 |
| 3.5 | 5.83232 | 5.93447 | 4.32577 | 6.03475 |
| 4 | 6.76491 | 6.83941 | 4.88219 | 6.57329 |
| 4.5 | 7.45890 | 7.78988 | 5.46104 | 7.93790 |
| 5 | 8.72351 | 8.78514 | 6.06307 | 8.95766 |
| 5.5 | 9.61206 | 9.82453 | 6.68908 | 10.02863 |
| 6 | 10.88496 | 10.90739 | 7.33986 | 11.11221 |
| 6.33 | 11.73176 | 11.64560 | 7.78335 | 11.83507 |
| 6.66 | 12.46271 | 12.40231 | 8.23825 | 12.60586 |
| 7 | 13.18708 | 13.20112 | 8.71912 | 13.41480 |
| 7.33 | 13.94953 | 13.99485 | 9.19794 | 14.24595 |
| 7.66 | 14.63634 | 14.80655 | 9.68894 | 15.08462 |
| 8 | 15.68905 | 15.66144 | 10.20784 | 15.97168 |
| 8.6 | 17.60833 | 17.21557 | 11.15668 | 17.52485 |
| 9.3 | 19.37477 | 19.10084 | 12.31910 | 19.43345 |
| 10 | 21.08035 | 21.06210 | 13.54388 | 21.46756 |
| 11 | 24.23921 | 23.99214 | 15.40756 | 24.38852 |
| 12.5 | 28.73396 | 28.65931 | 18.47271 | 28.96302 |
| 14 | 33.90510 | 33.63653 | 21.89078 | 33.93299 |
| 15 | 37.08741 | 37.11776 | 24.38320 | 37.35614 |
| 17.5 | 47.63629 | 46.35269 | 31.44756 | 46.46070 |
| 20 | 56.19024 | 56.28650 | 41.73355 | 55.79451 |
| 21.5 | 61.68744 | 62.55533 | 45.66534 | 62.39607 |
| 23.5 | 70.65006 | 71.25219 | 54.38566 | 71.14275 |
| 25 | 77.88609 | 78.01985 | 61.75071 | 77.70180 |
| 26.5 | 86.72389 | 84.99326 | 69.90130 | 84.62409 |
| 28.5 | 96.05846 | 94.60897 | 82.12687 | 93.82347 |
| 30 | 102.00735 | 102.05905 | 92.42754 | 100.39644 |
| 31.5 | 109.74730 | 109.713047 | 103.80504 | 107.45932 |
| 33.5 | 119.66223 | 120.23089 | 120.83548 | 116.14194 |
| 35 | 127.93239 | 128.34454 | 135.15732 | 124.48145 |
| 36.5 | 136.33310 | 136.63656 | 150.95244 | 131.17084 |
| 38.5 | 147.36690 | 147.93264 | 174.55657 | 141.23308 |
| 40 | 156.02669 | 156.54549 | 194.37665 | 148.79000 |
The prediction result shows that the BP neural network can correctly predict the value of the accumulation factor, the overall prediction effect is superior to the result calculated by the Taylor formula, and in a higher mfp interval, the result obtained by the BP neural network prediction is closer to the result of an ANSI database, and is better than the result obtained by MCNP simulation. The accumulation factor result predicted by the BP neural network can be used for determining the theoretical mask calculation.
The above is only a preferred embodiment of the method for calculating the accumulation factor of the shielding material based on the BP neural network, and the protection scope of the method for calculating the accumulation factor of the shielding material based on the BP neural network is not limited to the above embodiments, and all technical solutions belonging to the idea belong to the protection scope of the present invention. It should be noted that modifications and variations which do not depart from the gist of the invention will be those skilled in the art to which the invention pertains and which are intended to be within the scope of the invention.
Claims (7)
1. A method for calculating an accumulation factor of a shielding material based on a BP neural network is characterized by comprising the following steps: carrying out forward propagation, wherein the forward propagation process is that an input signal is transmitted to an output layer from an input layer through a hidden layer neuron, an output signal is generated at an output end, and when the output signal meets a given output requirement, the calculation is finished; when the output signal does not meet the given output requirement, the signal is shifted to the reverse propagation.
2. The method of claim 1, wherein the method comprises the following steps: the forward propagation of a neuron in a neural network is represented by:
wherein,
representing the input of the ith neuron of the jth layer, when j is the first layer, the input parameters are gamma ray energy E and mean free path mfp;
represents the input and weight w of the ith neuron of the j layeriThe sum of the products of (a) and (b) plus an offsetj(ii) a (z) represents an activation function,
representing the output of the j-th layer and simultaneously being used as the input of the j + 1-th layer, when the j-th layer is the output layer
For the final signal output value, when the network output value is not equal to the actual value, there is a loss value L, which is expressed by the following equation:
where y' is the expected value of the neural network output and y is the actual value.
3. The method of claim 2, wherein the method comprises the following steps: the neural network model uses the relu activation function, i.e., σ (z) ═ max { z,0 }.
4. The method of claim 2, wherein the method comprises the following steps: the neural network model may also employ sigmoid and tanh activation functions.
5. The method of claim 3, wherein the method comprises the following steps:
the BP training algorithm is adopted to optimize the weight by trying to minimize the sum of squared differences between the expected value and the actual value of the output neuron, and when L < ═ e, wherein e is the expected accuracy of calculation, or the learning times are preset, the calculation is finished, otherwise, the back propagation calculation is carried out.
6. The method of claim 5, wherein the method comprises the following steps: the BP algorithm calculates the weight w and the offset b to minimize L (w, b).
7. The method of claim 6, wherein the calculating the accumulation factor of the shielding material based on the BP neural network comprises: the principle of adjusting the weight is to reduce the error continuously, so the adjustment should be performed along the negative gradient direction of the weight, and the adjustment amount of the weight is proportional to the gradient decrease of the error by the following formula:
wherein, alpha represents the learning rate and is a given constant, 0 < alpha < 1;
representing the partial derivative of the loss function L to the ith input weight of the jth layer;
representing the partial derivative of the loss function L to the offset b of the j layer;
iteratively updating the neuron connection weight and the offset used for the next round of network learning and training according to the obtained change increment of the neuron connection weight and the offset of each layer, and updating by the following formula:
bj＝bj+Δbj
after solving new weight and offset of each layer, carrying out iterative calculation in the forward propagation process of steering;
the above is to complete a BP iteration loop, and when the calculated loss value L < ═ e is the set desired precision, and the smaller the setting, the higher the learning precision, the whole machine learning process is completed.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170091615A1 (en) * | 2015-09-28 | 2017-03-30 | Siemens Aktiengesellschaft | System and method for predicting power plant operational parameters utilizing artificial neural network deep learning methodologies |
| CN109978778A (en) * | 2019-03-06 | 2019-07-05 | 浙江工业大学 | Convolutional neural networks medicine CT image denoising method based on residual error study |
| CN110222844A (en) * | 2019-05-30 | 2019-09-10 | 西安交通大学 | A kind of compressor performance prediction technique based on artificial neural network |
| CN111489046A (en) * | 2019-01-29 | 2020-08-04 | 广东省公共卫生研究院 | Regional food safety evaluation model based on supply chain and BP neural network |
| CN111666719A (en) * | 2020-06-08 | 2020-09-15 | 南华大学 | Gamma radiation multilayer shielding accumulation factor calculation method, device, equipment and medium |
-
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170091615A1 (en) * | 2015-09-28 | 2017-03-30 | Siemens Aktiengesellschaft | System and method for predicting power plant operational parameters utilizing artificial neural network deep learning methodologies |
| CN111489046A (en) * | 2019-01-29 | 2020-08-04 | 广东省公共卫生研究院 | Regional food safety evaluation model based on supply chain and BP neural network |
| CN109978778A (en) * | 2019-03-06 | 2019-07-05 | 浙江工业大学 | Convolutional neural networks medicine CT image denoising method based on residual error study |
| CN110222844A (en) * | 2019-05-30 | 2019-09-10 | 西安交通大学 | A kind of compressor performance prediction technique based on artificial neural network |
| CN111666719A (en) * | 2020-06-08 | 2020-09-15 | 南华大学 | Gamma radiation multilayer shielding accumulation factor calculation method, device, equipment and medium |
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