The most common neural network model is the multi-layer perceptron (MLP). This type of neural network is known as a supervised network because it requires a desired output in order to learn. The goal of this type of network is to create a model that correctly maps the input to the output using historical data so that the model can then be used to produce the output when the desired output is unknown. In this paper, a new MLP is proposed for insemination problem. The result of the proposed method, is shown the high performance beside a very fast respond for the problem. Moreover, the conversion of the error is analyzed by the proposed method. All the simulation and result is done in MATLAB environments

Mirzaei, Farzad

Tashakori, Zeynolabedin

Munich Personal RePEc Archive

MPRA
Munich Personal RePEc Archive
Economic Approach for Stochastic
Artificial insemination by Neural
Network
Zeynolabedin Tashakori and Farzad Mirzaei
Electrical and Computer Engineering Department, Shiraz University
of Technology, Shiraz, Iran, Electrical and Computer Engineering
Department, Azad University of Marvdasht, Fars, Iran
6 October 2016
Online at https://mpra.ub.uni-muenchen.de/74339/
MPRA Paper No. 74339, posted 8 October 2016 14:12 UTC
Economic Approach for Stochastic Artificial insemination by 
Neural Network 
 
Zeynolabedin Tashakori1, Farzad Mirzaei2,  
 
1Electrical and Computer Engineering Department, Shiraz University of Technology, Shiraz, Iran 
2Electrical and Computer Engineering Department, Azad University of Marvdasht, Fars, Iran 
 
 
1. Abstract 
The most common neural network model is the multi-layer 
perceptron (MLP). This type of neural network is known as a 
supervised network because it requires a desired output in order 
to learn. The goal of this type of network is to create a model that 
correctly maps the input to the output using historical data so that 
the model can then be used to produce the output when the 
desired output is unknown. In this paper, a new MLP is proposed 
for insemination problem. The result of the proposed method, is 
shown the high performance beside a very fast respond for the 
problem. Moreover, the conversion of the error is analyzed by 
the proposed method. All the simulation and result is done in 
MATLAB environments. 
 
Keywords: A Multilayer Perceptron (MLP), Neural Network, 
Targets Train, Neuron, Targets Train. 
 
 
1. Introduction 
 
In the recent years, the applications of Neural Network 
(NN) are rapidly increased. This is mainly because such a 
systems can use in many kinds of problems such as power 
system [1], stability analysis [2], decentralize control [3], 
clustering data analysis [4], etc. For instance, in [5] fuzzy 
logic is used in high power voltage control for the first 
time but it is not perfect to low level frequency problems 
or low level electrical energies. In optimization problem 
neural network is used and modeled as the wind 
uncertainty in the main structure in [6]. The Mathematical 
modeling of neural network for stochastic and probabilistic 
problems is explained in [7], [8] which are perfect for 
renewable energy as well. In [9] for the first time neural 
network is used to model the random delay in control 
systems for the first time. Neural network has a lot of 
application in other science for nonlinear and non-convex 
problems [10-15]. 
Artificial insemination is a fertility treatment for humans, 
and is a common practice in the breeding of dairy 
cattle and pigs. Artificial insemination may 
employ assisted reproductive technology, donated sperm, 
and/or animal husbandry techniques. In this paper, I want 
to predict the probability of Artificial insemination by 
multi-layer perceptron (MLP) in neural network.  
In this paper, try to examine different numbers of neuron 
such as 10, 20, 30 in order to have a correct analysis of the 
problem. The results is proved that by increasing the 
neurons, the accuracy of the problem is better and the 
conversion error is very fast. It should be noted that  Mydir 
is a way which program is run and addpath  is a function 
which can add more way into the program by MATLAB 
neural network software. Also, by training more data or by 
increasing more test data, the neural network solution 
guarantee to coverage the error with a high performance 
results. After defined the neuron and layer of the neural 
network, the main insemination problem is applied to 
prove the high performance by MATLAB NN toolbox. 
Finally, incremental updating and batch updating are 
applied. The accuracy of the algorithm with different 
number of neurons and also True/false positive rate is 
analyzed and shown in the result section. In the second 
section, Multi-Layer Perceptron is defined. In the section 
three, artificial neuron and data are analyzed and 
explained. In the section four represented the results and 
discussion.   Finally, the conclusion is represented in 
section 5. 
 
2. Multi-layer Perceptron (MLP) 
 
A multilayer perceptron (MLP) is a feedforward artificial 
neural network model that maps sets of input data onto a 
set of appropriate outputs. An MLP consists of multiple 
layers of nodes in a directed graph, with each layer fully 
connected to the next one. Except for the input nodes, each 
node is a neuron (or processing element) with a 
nonlinear activation function. MLP utilizes as a supervised 
learning technique called back propagation for training the 
network. However, there are exists many other 
methods, MLP is a modification of the standard 
linear perceptron and can distinguish data that are 
not linearly separable. Moreover, MLP can guarantee the 
stability of the system and find the global solution for the 
problem. In this paper MLP is used for the neural network 
  
2 
model and the system based training of the neural network 
is MLP.  Fig.1 shows MLP in neural network. 
 
 
 
Fig 1. Multi-layer Perceptron (MLP). 
 
Based on the fig. 2 by increasing the number of layer in 
MLP, the accuracy of the system increased. However, the 
mathematical modeling of the system is become 
complicated. For this paper which has a lot of data, by 
increasing the number of the data, the performance of 
neural network is increased exponentially and the coverage 
of error was very fast.  
 
3. Artificial Neuron and Data 
An artificial neuron is a mathematical function conceived 
as a crude model, or abstraction of biological neurons. 
Artificial neurons are the constitutive units in an artificial 
neural network. The artificial neuron receives one or more 
inputs (representing the one or more dendrites) and sums 
them to produce an output (representing a biological 
neuron's axon). Usually the sums of each node are 
weighted, and the sum is passed through a non-
linear function known as an activation function or transfer 
function. In the artificial neuron and data, the number of 
neurons, training data and target data paly a very 
significant role in the behavior of the program and 
minimizing the time and coverage speed.  
3.1. Neurons Data 
In this paper, a part is defined as Neuron’s which you can 
determine the number of neurons that you need for your 
estimation.  So, it is possible to change the number of 
neurons according to the demand (which is estimated or 
predicted before the programming) and your valid data.  
In such kind of program, the best program is a program 
that no changes happen in your final result for any different 
number of neurons. It means the program saturated very 
fast and coverage the error in the worst case scenario. As a 
result, the number of neurons could change according to 
the demand and possible data. In the result section, the 
above statement is analyzed and proved for different 
number of neurons and defined it as an accuracy of 
algorithm with different number of neurons. 
 
3.2. Targets Train 
 For writing a program it is need some correct sample data 
which are test and experienced before. So by referring to 
them as a pattern for other data in order to figure out that 
the available data (current data) are belong to what kinds 
of groups. In this paper, two different groups is defined 
and analyzed: 1) True 2) False. In fact, by selecting some 
correct data as a pattern for future data. This method help 
to compare to both kinds of data for take a decision as a 
result. In this program a set of data is used as a pattern 
which you can see in the program as targets train. In fact, 
targets trains are a goal for the result.  
 
4. Case study and Results 
In this paper, it is possible to change parameters according 
to the interest. Also, it is possible to add any new 
experience into the basic data for further information’s. 
For example, this case study used 283 data for get true 
result. Considering that new data update to 1000 
information. Therefore, easily can add them to the current 
results for better production especially when the goal is 
mixed up two different phylum’s of animals (for example 
cattle of Asian and cattle of USA) for low risk and high 
economic efficiency. Also easily can update the program 
for produce better phylum’s and organic productions. 
The accuracy of the neural network program is shown in 
fig 2 and fig 3 for different number of neurons. The result 
prove the high performance of the proposed technique. 
 
 
 
Fig. 2 Accuracy of Algorithm for 10 Number of Neurons. 
 
  
3 
 
 
Fig 3 Accuracy of Algorithm for 20 Number of Neurons. 
 
 
Three kinds of group’s are defined as three different 
colors. The first groups are dark green and light green (to 
some extant light yellow) which are represent that those 
kind of phylum’s are ready for reproduce in a safe 
condition Fig 4. However, dark green are much better than 
light green. Therefore, investing on them is very logical 
and far from any stress and strain.  
 
 
 
Fig 4 First Groups (No risk). 
 
The second groups are belongs to the light read which 
means that insemination is in the risk Fig 5. So, for 
investment should get sample again or analyze the 
condition for reproduce due to they are in border line. The 
third groups are dark red and dark blue (other colors) 
which represent that insemination are in the high risk and 
they are not ready for insemination and also not proper for 
investment. As a result, we should reject them and invest in 
the other groups Fig 6. 
 
 
 
Fig. 5 Second Groups (Low risk). 
 
 
 
 
 
Fig 6 Third Groups (Full risk). 
 
 
In the final result, the true or false rate is defined based on 
the program. For instance, if the selection is in the correct 
way the plot and result proved the accuracy of the selection 
otherwise the false result is presented. This part of the 
program, shown the intelligent behavior of the neural 
network compare to other methods. Figure 7 shown an 
example of that part. 
  
4 
 
 
 
Fig 7. Result for True or False Rate. 
 
5. Conclusions 
 
It is not possible to guarantee that all of the artificial 
program is true in practical because many factors maybe 
change at the same time and always we have exceptional 
event in the environment. But, by prediction the problems 
and solve it by many simulation and analyzer software it is 
possible to compare all techniques. Among many science, 
neural network is a very powerful science that can show 
the approximate result in a very perfect possible way. In 
this paper, tried to show one aspect of neural network in 
practical side of this science. This kind of program is very 
useful for the future of the machine learning, smart grids 
and cyber-physical systems. 
 
References 
[1] S. Mehraeen, S. Jagannathan and M. L. Crow, "Power 
System Stabilization Using Adaptive Neural Network-Based 
Dynamic Surface Control", in IEEE Transactions on Power 
Systems, Vol. 26, No. 2, 2011, pp. 669-680. 
[2] H. Qiao, J. Peng, Z. B. Xu and B. Zhang, "A Reference 
Model Approach to Stability Analysis of Neural Networks", 
in IEEE Transactions on Systems, Man, and Cybernetics, 
Part B (Cybernetics), Vol. 33, No. 6, 2003, pp. 925-936. 
[3] S. Huang, K. K. Tan and T. H. Lee, "Decentralized Control 
Design for Large-scale Systems with Strong Interconnections 
Using Neural Networks", in IEEE Transactions on Automatic 
Control, Vol. 48, No. 5, 2003, pp. 805-810. 
[4] B. Karlik, M. O. Tokhi and M. Alci, "A fuzzy Clustering 
Neural Network Architecture for Multifunction Upper-limb 
Prosthesis", in IEEE Transactions on Biomedical 
Engineering, Vol. 50, No. 11, 2003, pp. 1255-1261. 
[5] Dabbaghjamanesh, M., A. Moeini, M. Ashkaboosi, P. 
Khazaei, and K. Mirzapalangi. "High performance control of 
grid connected cascaded H-Bridge active rectifier based on 
type II-fuzzy logic controller with low frequency modulation 
technique." International Journal of Electrical and Computer 
Engineering (IJECE) 6, no. 2 (2016): 484-494. 
[6] Ashkaboosi, Maryam, Seyed Mehdi Nourani, Peyman 
Khazaei, Morteza Dabbaghjamanesh, and Amirhossein 
Moeini. "An Optimization Technique Based on Profit of 
Investment and Market Clearing in Wind Power 
Systems."American Journal of Electrical and Electronic 
Engineering 4, no. 3 (2016): 85-91. 
[7] Zakernejad, Mohammad Hossein, Mohammad Mohammadi, 
and Mahdi Mehrtash. "A New Approach for Probabilistic 
Evaluation of Transient Recovery Voltage Across Circuit 
Breakers in TCSC Compensated Transmission Lines." 
In Power System Conference (PSC), Tehran. 2014. 
[8] Mehrtash, Mahdi, Masoud Jokar Kouhanjani, Amir Pourjafar, 
and Seyedbehnam Beladi. "An Interior Point Optimization 
Method for Stochastic Security-constrained Unit 
Commitment in the Presence of Plug-in Electric 
Vehicles." Journal of Applied Sciences 16, no. 5 (2016): 189. 
[9] M. Rakhshan, N. Vafamand, M. Shasadeghi, M. 
Dabbaghjamanesh, A. Moeini, "Design of Networked 
Polynomial Control Systems with Random Delays: Sum of 
Squares Approach", Int. J. Automation and Control, Vol. 10, 
No. 1, 2016, pp. 73- 86. 
[10] Mehrtash, Mahdi, Masoud Jokar Kouhanjani, and 
Mohammad Mohammadi. "A new nonparametric density 
estimation for probabilistic security-constrained economic 
dispatch." Journal of Intelligent & Fuzzy SystemsPreprint: 1-
12. 
[11] Mehraeen, Shahab, Sarangapani Jagannathan, and Keith A. 
Corzine. "Energy harvesting from vibration with alternate 
scavenging circuitry and tapered cantilever beam." IEEE 
Transactions on Industrial Electronics 57, no. 3 (2010): 820-
830. 
[12] Ghaffari, Saeed, and M. Ashkaboosi. "Applying Hidden  
Markov M Recognition Based on C." (2016). 
[13] Ghaffari, Saeed, and Maryam Ashkaboosi. "Applying 
Hidden Markov Model Baby Cry Signal Recognition Based 
on Cybernetic Theory." IJEIR 5, no. 3 (2016): 243-247. 
[14] Hung, Ying-Chih, Faa-Jeng Lin, Jonq-Chin Hwang, Jin-
Kuan Chang, and Kai-Chun Ruan. "Wavelet fuzzy neural 
network with asymmetric membership function controller for 
electric power steering system via improved differential 
evolution." IEEE Transactions on Power Electronics 30, no. 
4 (2015): 2350-2362. 
[15] Lin, Faa-Jeng, Kuang-Chin Lu, Ting-Han Ke, Bo-Hui Yang, 
and Yung-Ruei Chang. "Reactive power control of three-
phase grid-connected PV system during grid faults using 
Takagi–Sugeno–Kang probabilistic fuzzy neural network 
control." IEEE Transactions on Industrial Electronics 62, no. 
9 (2015): 5516-5528. 
 


Economic Approach for Stochastic Artificial insemination by Neural Network

Munich RePEc Personal Archive

Munich Personal RePEc ArchiveEconomic Approach for StochasticArtificial insemination by NeuralNetworkTashakori, Zeynolabedin and Mirzaei, FarzadElectrical and Computer Engineering Department, Shiraz Universityof Technology, Shiraz, Iran, Electrical and Computer EngineeringDepartment, Azad University of Marvdasht, Fars, Iran6 October 2016Online at https://mpra.ub.uni-muenchen.de/74339/MPRA Paper No. 74339, posted 08 Oct 2016 14:12 UTCEconomic Approach for Stochastic Artificial insemination by Neural Network  Zeynolabedin Tashakori1, Farzad Mirzaei2,   1Electrical and Computer Engineering Department, Shiraz University of Technology, Shiraz, Iran 2Electrical and Computer Engineering Department, Azad University of Marvdasht, Fars, Iran   1. Abstract The most common neural network model is the multi-layer perceptron (MLP). This type of neural network is known as a supervised network because it requires a desired output in order to learn. The goal of this type of network is to create a model that correctly maps the input to the output using historical data so that the model can then be used to produce the output when the desired output is unknown. In this paper, a new MLP is proposed for insemination problem. The result of the proposed method, is shown the high performance beside a very fast respond for the problem. Moreover, the conversion of the error is analyzed by the proposed method. All the simulation and result is done in MATLAB environments.  Keywords: A Multilayer Perceptron (MLP), Neural Network, Targets Train, Neuron, Targets Train.   1. Introduction  In the recent years, the applications of Neural Network (NN) are rapidly increased. This is mainly because such a systems can use in many kinds of problems such as power system [1], stability analysis [2], decentralize control [3], clustering data analysis [4], etc. For instance, in [5] fuzzy logic is used in high power voltage control for the first time but it is not perfect to low level frequency problems or low level electrical energies. In optimization problem neural network is used and modeled as the wind uncertainty in the main structure in [6]. The Mathematical modeling of neural network for stochastic and probabilistic problems is explained in [7], [8] which are perfect for renewable energy as well. In [9] for the first time neural network is used to model the random delay in control systems for the first time. Neural network has a lot of application in other science for nonlinear and non-convex problems [10-15]. Artificial insemination is a fertility treatment for humans, and is a common practice in the breeding of dairy cattle and pigs. Artificial insemination may employ assisted reproductive technology, donated sperm, and/or animal husbandry techniques. In this paper, I want to predict the probability of Artificial insemination by multi-layer perceptron (MLP) in neural network.  In this paper, try to examine different numbers of neuron such as 10, 20, 30 in order to have a correct analysis of the problem. The results is proved that by increasing the neurons, the accuracy of the problem is better and the conversion error is very fast. It should be noted that  Mydir is a way which program is run and addpath  is a function which can add more way into the program by MATLAB neural network software. Also, by training more data or by increasing more test data, the neural network solution guarantee to coverage the error with a high performance results. After defined the neuron and layer of the neural network, the main insemination problem is applied to prove the high performance by MATLAB NN toolbox. Finally, incremental updating and batch updating are applied. The accuracy of the algorithm with different number of neurons and also True/false positive rate is analyzed and shown in the result section. In the second section, Multi-Layer Perceptron is defined. In the section three, artificial neuron and data are analyzed and explained. In the section four represented the results and discussion.   Finally, the conclusion is represented in section 5.  2. Multi-layer Perceptron (MLP)  A multilayer perceptron (MLP) is a feedforward artificial neural network model that maps sets of input data onto a set of appropriate outputs. An MLP consists of multiple layers of nodes in a directed graph, with each layer fully connected to the next one. Except for the input nodes, each node is a neuron (or processing element) with a nonlinear activation function. MLP utilizes as a supervised learning technique called back propagation for training the network. However, there are exists many other methods, MLP is a modification of the standard linear perceptron and can distinguish data that are not linearly separable. Moreover, MLP can guarantee the stability of the system and find the global solution for the problem. In this paper MLP is used for the neural network   2 model and the system based training of the neural network is MLP.  Fig.1 shows MLP in neural network.    Fig 1. Multi-layer Perceptron (MLP).  Based on the fig. 2 by increasing the number of layer in MLP, the accuracy of the system increased. However, the mathematical modeling of the system is become complicated. For this paper which has a lot of data, by increasing the number of the data, the performance of neural network is increased exponentially and the coverage of error was very fast.   3. Artificial Neuron and Data An artificial neuron is a mathematical function conceived as a crude model, or abstraction of biological neurons. Artificial neurons are the constitutive units in an artificial neural network. The artificial neuron receives one or more inputs (representing the one or more dendrites) and sums them to produce an output (representing a biological neuron's axon). Usually the sums of each node are weighted, and the sum is passed through a non-linear function known as an activation function or transfer function. In the artificial neuron and data, the number of neurons, training data and target data paly a very significant role in the behavior of the program and minimizing the time and coverage speed.  3.1. Neurons Data In this paper, a part is defined as Neuron’s which you can determine the number of neurons that you need for your estimation.  So, it is possible to change the number of neurons according to the demand (which is estimated or predicted before the programming) and your valid data.  In such kind of program, the best program is a program that no changes happen in your final result for any different number of neurons. It means the program saturated very fast and coverage the error in the worst case scenario. As a result, the number of neurons could change according to the demand and possible data. In the result section, the above statement is analyzed and proved for different number of neurons and defined it as an accuracy of algorithm with different number of neurons.  3.2. Targets Train  For writing a program it is need some correct sample data which are test and experienced before. So by referring to them as a pattern for other data in order to figure out that the available data (current data) are belong to what kinds of groups. In this paper, two different groups is defined and analyzed: 1) True 2) False. In fact, by selecting some correct data as a pattern for future data. This method help to compare to both kinds of data for take a decision as a result. In this program a set of data is used as a pattern which you can see in the program as targets train. In fact, targets trains are a goal for the result.   4. Case study and Results In this paper, it is possible to change parameters according to the interest. Also, it is possible to add any new experience into the basic data for further information’s. For example, this case study used 283 data for get true result. Considering that new data update to 1000 information. Therefore, easily can add them to the current results for better production especially when the goal is mixed up two different phylum’s of animals (for example cattle of Asian and cattle of USA) for low risk and high economic efficiency. Also easily can update the program for produce better phylum’s and organic productions. The accuracy of the neural network program is shown in fig 2 and fig 3 for different number of neurons. The result prove the high performance of the proposed technique.    Fig. 2 Accuracy of Algorithm for 10 Number of Neurons.    3   Fig 3 Accuracy of Algorithm for 20 Number of Neurons.   Three kinds of group’s are defined as three different colors. The first groups are dark green and light green (to some extant light yellow) which are represent that those kind of phylum’s are ready for reproduce in a safe condition Fig 4. However, dark green are much better than light green. Therefore, investing on them is very logical and far from any stress and strain.     Fig 4 First Groups (No risk).  The second groups are belongs to the light read which means that insemination is in the risk Fig 5. So, for investment should get sample again or analyze the condition for reproduce due to they are in border line. The third groups are dark red and dark blue (other colors) which represent that insemination are in the high risk and they are not ready for insemination and also not proper for investment. As a result, we should reject them and invest in the other groups Fig 6.    Fig. 5 Second Groups (Low risk).      Fig 6 Third Groups (Full risk).   In the final result, the true or false rate is defined based on the program. For instance, if the selection is in the correct way the plot and result proved the accuracy of the selection otherwise the false result is presented. This part of the program, shown the intelligent behavior of the neural network compare to other methods. Figure 7 shown an example of that part.   4    Fig 7. Result for True or False Rate.  5. Conclusions  It is not possible to guarantee that all of the artificial program is true in practical because many factors maybe change at the same time and always we have exceptional event in the environment. But, by prediction the problems and solve it by many simulation and analyzer software it is possible to compare all techniques. Among many science, neural network is a very powerful science that can show the approximate result in a very perfect possible way. In this paper, tried to show one aspect of neural network in practical side of this science. This kind of program is very useful for the future of the machine learning, smart grids and cyber-physical systems.  References [1] S. Mehraeen, S. Jagannathan and M. L. Crow, "Power System Stabilization Using Adaptive Neural Network-Based Dynamic Surface Control", in IEEE Transactions on Power Systems, Vol. 26, No. 2, 2011, pp. 669-680. [2] H. Qiao, J. Peng, Z. B. Xu and B. Zhang, "A Reference Model Approach to Stability Analysis of Neural Networks", in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol. 33, No. 6, 2003, pp. 925-936. [3] S. Huang, K. K. Tan and T. H. Lee, "Decentralized Control Design for Large-scale Systems with Strong Interconnections Using Neural Networks", in IEEE Transactions on Automatic Control, Vol. 48, No. 5, 2003, pp. 805-810. [4] B. Karlik, M. O. Tokhi and M. Alci, "A fuzzy Clustering Neural Network Architecture for Multifunction Upper-limb Prosthesis", in IEEE Transactions on Biomedical Engineering, Vol. 50, No. 11, 2003, pp. 1255-1261. [5] Dabbaghjamanesh, M., A. Moeini, M. Ashkaboosi, P. Khazaei, and K. Mirzapalangi. "High performance control of grid connected cascaded H-Bridge active rectifier based on type II-fuzzy logic controller with low frequency modulation technique." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 2 (2016): 484-494. [6] Ashkaboosi, Maryam, Seyed Mehdi Nourani, Peyman Khazaei, Morteza Dabbaghjamanesh, and Amirhossein Moeini. "An Optimization Technique Based on Profit of Investment and Market Clearing in Wind Power Systems."American Journal of Electrical and Electronic Engineering 4, no. 3 (2016): 85-91. [7] Zakernejad, Mohammad Hossein, Mohammad Mohammadi, and Mahdi Mehrtash. "A New Approach for Probabilistic Evaluation of Transient Recovery Voltage Across Circuit Breakers in TCSC Compensated Transmission Lines." In Power System Conference (PSC), Tehran. 2014. [8] Mehrtash, Mahdi, Masoud Jokar Kouhanjani, Amir Pourjafar, and Seyedbehnam Beladi. "An Interior Point Optimization Method for Stochastic Security-constrained Unit Commitment in the Presence of Plug-in Electric Vehicles." Journal of Applied Sciences 16, no. 5 (2016): 189. [9] M. Rakhshan, N. Vafamand, M. Shasadeghi, M. Dabbaghjamanesh, A. Moeini, "Design of Networked Polynomial Control Systems with Random Delays: Sum of Squares Approach", Int. J. Automation and Control, Vol. 10, No. 1, 2016, pp. 73- 86. [10] Mehrtash, Mahdi, Masoud Jokar Kouhanjani, and Mohammad Mohammadi. "A new nonparametric density estimation for probabilistic security-constrained economic dispatch." Journal of Intelligent & Fuzzy SystemsPreprint: 1-12. [11] Mehraeen, Shahab, Sarangapani Jagannathan, and Keith A. Corzine. "Energy harvesting from vibration with alternate scavenging circuitry and tapered cantilever beam." IEEE Transactions on Industrial Electronics 57, no. 3 (2010): 820-830. [12] Ghaffari, Saeed, and M. Ashkaboosi. "Applying Hidden  Markov M Recognition Based on C." (2016). [13] Ghaffari, Saeed, and Maryam Ashkaboosi. "Applying Hidden Markov Model Baby Cry Signal Recognition Based on Cybernetic Theory." IJEIR 5, no. 3 (2016): 243-247. [14] Hung, Ying-Chih, Faa-Jeng Lin, Jonq-Chin Hwang, Jin-Kuan Chang, and Kai-Chun Ruan. "Wavelet fuzzy neural network with asymmetric membership function controller for electric power steering system via improved differential evolution." IEEE Transactions on Power Electronics 30, no. 4 (2015): 2350-2362. [15] Lin, Faa-Jeng, Kuang-Chin Lu, Ting-Han Ke, Bo-Hui Yang, and Yung-Ruei Chang. "Reactive power control of three-phase grid-connected PV system during grid faults using Takagi–Sugeno–Kang probabilistic fuzzy neural network control." IEEE Transactions on Industrial Electronics 62, no. 9 (2015): 5516-5528.  

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Economic Approach for Stochastic Artificial insemination by Neural Network

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