Expert system design for elastic scattering neutrons optical model using bpnn

International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.1, February 2014
DOI:10.5121/ijcsa.2014.4 101 1
Expert System Design for Elastic Scattering
Neutrons Optical Model using BPNN
FADHIL A. ALI
Department of Electrical and Computer Engineering- Oklahoma State University
202 Engineering South Stillwater, OK 74078 USA- Tel:405-714-1084
ABSTRACT
In present paper, a proposed expert system is designed to obtain a trained formulae for the optical model
parameters used in elastic scattering neutrons of light nuclei for (7
Li), at energy range between [(1) to
(20)] MeV. A simple algorithm has used to design this expert system, while a multi-layer backward-
propagation neural network (BPNN) is applied for training and testing the data used in this model. This
group of formulae may get a simple expert system occurring from governing formulae model, and predicts
the critical parameters usually resulted from the complicated computer coding methods. This expert system
may use in nuclear reactions yields in both fission and fusion nature who gives more closely results to the
real model.
KEYWORDS
Expert systems, optical model analysis, software engineering applications, neural networks
1. Introduction
The scattering of a neutron by a nucleus is the result of a very complicated series of interactions
of direct and indirect reactions between the neutron and the nucleons of the target nucleus. It
rather surprising this can be represented only by a simple optical potential, which is called an
optical model. The optical potential which was employed throughout this study is:
V(r) = - Vr f(r, Rr, Ar) + i4Wd Ad f (r, Rd, Ad) + [ Vso f (r, Rso, Aso) ] ( )2
σ.l ………(1)
Where:
f( r, R, A) = [1+exp[(r-R)/A]]-1
…………………..…(2)
Thus the expression (2) is Saxon-Woods form factor which is the first approximation that
considers the optical potential to have a radial variation that follows the nuclear density quite
closely [1]. The numerical values of these parameters are calculated for this investigation, which
observed in equation (1), as follows:
1. Potential parameters:
i- Real potential "Vr "
ii- Volume – imaginary potential "Wv"
iii- Surface – imaginary potential "Ws"
iv- Spin – orbit potential "Vso"
Where:"Wv" and "Ws" can be represented as "Wd".

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2. Radius parameters:
i- Real radius "Rr"
ii- Volume – imaginary radius" Rv"
iii- Surface – imaginary radius" Rs"
iv- Spin – orbit radius "Rso"
Where:"Rv" and "Rs" can be represented as "Rd"
2. Diffuseness parameters:
i- Real diffuseness "Ar"
ii- Volume – imaginary diffuseness "Av"
iii- Surface – imaginary diffuseness "As"
iv- Spin – orbit diffuseness "Aso"
Where:"Av" and "As" can be represented as "Ad". Also, the expression "( )2
" is the square of
the pion Compton wavelength." [1, 2]
2. Proposed system
2.1 Data collection
Perey & Perey Tables [3] have been used to collect the present optical model parameters data
were available for 7
Li nucleus, by scattered neutrons at energy range (1-20) MeV. This study was
extended to provide more data till the end of 2010.
2.2 Program Design (Coding)
An expert system is a software package containing at least a knowledgebase, for reasoning unit
and man–machine interfaces, it is used to create and design the present expert algorithm. Its code
is written to solve the parameters in equations (1) & (2). The C# language is used in coding the
expert system nuggets. Since the potential parameters vary linearly with energy [E] and nuclear
asymmetry [(N-Z)/A]; where N is the number of neutrons and Z is the number of protons.
Similarly, radius parameters and diffuseness parameters are vary with mass number (A1/3
) and
nuclear asymmetry; have analyzed them according to the same routine. This work have included
those parameters by writing: [2]
V = V0 + ϵE + Vi [(N-Z)/A]……..…………….…… (3)
R = R0 + ɣ A1/3
+ Ri [(N-Z)/A] ……….….………… (4)
A = A0 + β A1/3
+ Ai [(N-Z)/A] ……….…………… (5)
Where; V0,Vi, R0, A0, ϵ, ɣ and β are the coefficients of the potential, radius and diffuseness
parameters, respectively. The expert system rules written have a data-driven program, it has taken
procedure in [4], where the facts are the data as (text files) stored in our knowledgebase engine.
This engine decides which rule should be executed. Therefore, the present expert system
automatically performs the optical model parameters, according to the following:
• It has 3 facts being analyzed (potential, radius and diffuseness parameters) equations (3,
4, and 5) respectively.

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• It has 36 rules of dynamic programming procedure codes, which obey the coefficients
(V0,Vi, R0, A0, ϵ, ɣ and β )and constraints (energy [E] ,nuclear asymmetry [(N-Z)/A] and
mass number (A1/3
) equation (1).
2.3 Algorithm
Figure (1) is an algorithm of the present expert system, which shows available data, calculated the
optical model parameters.
Figure (1) shows the proposed expert system algorithm

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2.4 The Neural network
For an artificial expert system ANN, reliability and applicability are the two most important
factors to be taken into consideration even at the first stage of development. The backward
propagation neural network (BPNN) model has been applied and reported well in industries [5].
The present parameters are tested using the available Perey & Perey data Tables [3]. The
application of BPN, as shown in Figure 2, involves the calculation of the error between the
network output vector and the target vector. Generally, let the BPN have the input vector x of
length Nin; the network output vector y of length r; and the synaptic weight matrix W [5].
Figure (2) shows schematic diagram of the BPN Architecture
Then a transfer function maps x into y by
y= (WT
x - θ) = ( ) ………………….. (6)
Where θ is the bias that is used to mimic the threshold value of the axon, below which the neuron
would not respond, the output net activity vector.
For BPN with a supervised learning process, there exist two distinct computation passes. The first
one is referred to as the ‘forward pass’ in which the synaptic weight matrix remains unchanged.
In other words, the information inputs pass forward to the output. On the other hand, the second
pass is called the ‘backward pass’ where the error is passing backwards starting from the
outermost layer. Thus, through the recursive computation for each neuron the weight matrix
undergoes modifications. Or, let the (n+1)th change of weight matrix be W(n+1). Then
W (n+1) = W (n) + Δ W (n)……………….. (7)
Where Δ W (n) is the weight adjustment matrix obtained from the last change."[5]
"The learning mechanism of the backward propagation networks is a generalized delta rule that
performs a gradient descent on the error space to minimize the total error between the calculated

5
the desired one of an output layer during modification of connection weight. The least mean
square error is carried out to find the values the connection weights that minimize the error
function by resilient backward propagation method. During the process of learning the mean
square error (MSE) is monitored the network instantaneously to achieve better understating of the
network performance. The MSE can be calculated as;
MSE= ( + ) ……………………. (8)
Where, is the actual value, is the predicted value and is the number of values." [6]
This study has collected a selective range of data provided by Perey & Perey Tables [3]; these
data include 100 results of the elastic scattered neutrons of 7
Li for energy range (1-20) MeV.
Among the collected data 75 selected randomly are used as training data, while the remaining 25
are regarded as tested data.
Presently, optical model has three major parameters
• Potential parameters: [Vr, Wv, Ws, Vso]
• Radius parameters [Rr, Rv, Rs, Rso ]
• Diffuseness parameters[ Ar, Av, As, Aso]
Therefore, 12 parameters are used and represented as input layer of the neural network nodes
(units). The output layer includes one neuron representing the ultimate moment capacity of the
elastic scattered neutron beams of 7
Li for energy range (1-20) MeV.
3. Results and discussion
3.1 Training and testing of network
The present network configuration has achieved after watching the performance of different
configurations. Thus, learning parameters and processes were changed and repeated due to the
same procedure. To avoid any over-training, a convergence criterion adopted by present work
depending on whether the MSE of testing data has reached its minimum or not.
The present neural network is trained, therefore the slow rate of learning near the end points of
data range is avoided, the inputs and outputs data were scaled into an interval of [-1,1] by using
the minimum and maximum method. The values of the present network parameters considered
the following;
• Number of hidden layers = 2
• Number of units in first hidden layer = 12
• Number of units in second hidden layer = 10
• Training cycles = 10000
• Initial weights = 0.3
• Learning rate = 0.3
• Momentum = 0.9
The average MSE for training and testing is 0.000287 and 0.00041 respectively. Figure 3 shows
the convergence of network for both training represents as ultimate optical model parameters
data, and testing represents as optical model data.

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Figure (3) shows the convergence of present network
Table (1) shows the MSE values for each optical model parameters data in both training and
testing respectively. Thus, table (2) shows an optical model parameters predicted values resulted
by the proposed expert system. This will get new set formulae represented the optical model, and
table (3) has shown the set formulae depending on the predicted values of the proposed expert
system. Since nuclear asymmetry has fixed value for 7
Li, which equals to (0.14) as well as mass
number 7, the parameters were not affected dynamically rather than energy range dependences.
Table (1) shows the MSE values for each optical model parameters data
Optical
Parameters
MSE of
Training
Data
MSE of
Testing Data
Vr 0.00013 0.00015
Rr 0.00040 0.000143
Ar 0.00030 0.000409
Wv 0.000433 0.001436
Rv 0.000450 0.000108
Av 0.00080 0.000525
Ws 0.00090 0.000744
Rs 0.000006 0.000130
As 0.000150 0.000510
Vso 0.000194 0.003260
Rso 0.000093 0.000162
Aso 0.000310 0.000248

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Table (2) shows parameters predicted values
Present
Optical
Model1
Predicted Values
Potential Parameters
Energy
(MeV)
Vr Wv Ws Vso
1 47.24 5.25 9.60 9.26
2 47.27 4.98 9.80 9.07
3 47.29 4.71 9.99 8.88
4 47.31 4.44 10.19 8.69
5 47.34 4.17 10.38 8.50
6 47.36 3.90 10.57 8.31
7 47.38 3.63 10.77 8.12
8 47.41 3.36 10.96 7.93
9 47.43 3.09 11.16 7.74
10 47.45 2.82 11.35 7.55
11 47.47 2.55 11.54 7.36
12 47.50 2.28 11.74 7.17
13 47.52 2.01 11.93 6.98
14 47.54 1.74 12.13 6.79
15 47.57 1.47 12.32 6.60
16 47.59 1.20 12.51 6.41
17 47.61 0.93 12.71 6.22
19 47.64 0.66 12.90 6.03
20 47.66 0.39 13.10 5.84
Present
Optical
Model2
Radius Parameters
Rr Rv Rs Rso
1.27 1.26 1.21 1.11
Present
Optical
Model3
Diffuseness Parameters
Ar Av As Aso
0.55 0.58 0.38 0.88

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Table (3) shows the new empirical set formulae of the present optical model compact expert system
Optical
Parameters
Predicted empirical formula
E: Energy (MeV), A: Mass Number and α : Nuclear Asymmetry (N-Z)/A
Vr 47.24+0.023E-0.13α
Rr 1.198+0.01A1/3
+0.38α
Ar 0.5+0.036 A1/3
-0.164α
Wv 5.58-0.27E-0.41α
Rv 1.27-0.007 A1/3
+0.003α
Av 0.09+0.195 A1/3
+0.8α
Ws 5.66+0.194E+26.79α
Rs 1.17+0.019 A1/3
As 0.69-2.25α
Vso 8.7-0.19E+5.34α
Rso 1.21-0.75α
Aso 0.58+2.12α
In compare the present formulae resulted with others, table (4) shows both present results and
Dave et al [7] in order to give the global representation of this proposed expert system.
Table 4 shows the empirical set formulae of Dave et al
Optical
Parameters
Dave et al Formulae
Vr 54.14-0.02E-23.48α
Rr 1.508-0.013A
Ar 0.5
Wv 11.32+0.237E-16.08α
Rv 1.353
Av 0.2
Ws 11.32+0.237E-16.08α
Rs 1.353
As 0.2
Vso 5.5
Rso 1.15
Aso 0.5
3.2 Discussions
For the light nucleus of 7
Li, the nuclear asymmetry (N-Z)/A has value equals to 0.14, that might
give both radius and diffuseness parameters fixed values depending on the empirical formulae as
in table 2. These parameters are depended on mass number A and nuclear asymmetry α. Incident

9
neutrons energy (1-20) MeV does not affect the values of such parameters. Potential parameters
have functions depending on incident neutron energy and nuclear asymmetry.
It can be seen that BPN gives the smallest MSE values for both training and testing data. And it is
clear that all optical parameters are functions of energy, mass number and nuclear asymmetry.
While it cannot be seen these factors in Dave et al formulae set.
The 36 rules written in comparison to the predictions of the BPN results, and it give the expert
system to show the ability for taken the map of decisions made. As fit all results that BPN gets
strong and precise model for nuclear reactions used as in [8].
4. Conclusion
In this study, it is found that backward propagation neural network embedded model with 36 rules
and 3 facts are effective way for analyze of an optical model for elastic scattered neutrons. The
configuration of 12-10 nodes in the first and second hidden layer is proved to be very efficient for
predicting the ultimate optical parameters. In addition, MSE has acceptable values in both
training and testing data of the network.
Mass number A1/3
, incident neutron energy E and nuclear asymmetry (N-Z)/A are found by
predictions to have large influence on optical model parameters.
Potential parameters (Vr,Wv,Ws and Vso) are found to be energy E and nuclear asymmetry
dependent, while radius and diffuseness parameters (Rr,Ar,Rv, and Av) are mass number A and
nuclear asymmetry dependent. Except for Rs which is mass number dependent only, similarly
(As,Rso and Aso) are nuclear asymmetry dependent only.
Acknowledgement
I would like to thank Iraq Scholar Rescue Project/Scholar Rescue Fund / Institute of International
Education group for any kind of help, adding to Oklahoma State University / school of ECEN.
References
[1] R.L. Cassola & R.D. Koshel, the neutron-nucleus interaction, IL NUOVO CIMENTO B (1965-1970),
Volume 47, Number 2 ,1967 , pp.303-305
[2] P.E. Hodgson, Rep. Prog. Phys., 47, 1984, pp.613-654
[3] C.M. Perey & F.G. Perey, Atomic Data and Nuclear Data Tables, Volume 17, Issue 1, January
1976,pp. 1–101
[4] John Paul Mueller," C# Design and Development: Expert One on One", John Wiley & Sons, 2009
[5] Li Wenlung , Y.P. Tsaib & C.L. Chiu, The experimental study of the expert system for diagnosing
unbalances by ANN and acoustic signals, Journal of Sound and Vibration, 272,2004,pp.69–83
[6] M. Riedmiller & H. Braun, A direct Adaptive Method for Faster Back propagation learning: The
RPROP Algorithm, IEEE international Conference on Neural Networks, 1993, pp.586-591
[7] J.H. Dave & C.R. Gould, Optical model analysis of scattering of 7- to 15-MeV neutrons from 1-p
shell nuclei, Physical Review C, 28, Issue 6, 1983,pp.2212-2221
[8] Subhra Rani Patra et al, Artificial Neural Network Model for Intermediate Heat Exchanger of Nuclear
Reactor,International Journal of Computer applications (0975 - 8887) Volume 1 – No. 26, 2010

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