scholarly journals Solving Mixed Volterra - Fredholm Integral Equation (MVFIE) by Designing Neural Network

2019 ◽  
Vol 16 (1) ◽  
pp. 0116
Author(s):  
Al-Saif Et al.
Keyword(s):  
Neural Network ◽  
Integral Equation ◽  
Analytic Solution ◽  
Fredholm Integral Equations ◽  
Training Algorithm ◽  
Output Unit ◽  
Feed Forward Neural Network ◽  
Levenberg Marquardt ◽  
Hidden Layer ◽  
And Training

       In this paper, we focus on designing feed forward neural network (FFNN) for solving Mixed Volterra – Fredholm Integral Equations (MVFIEs) of second kind in 2–dimensions. in our method, we present a multi – layers model consisting of a hidden layer which has five hidden units (neurons) and one linear output unit. Transfer function (Log – sigmoid) and training algorithm (Levenberg – Marquardt) are used as a sigmoid activation of each unit. A comparison between the results of numerical experiment and the analytic solution of some examples has been carried out in order to justify the efficiency and the accuracy of our method.                                  

Viscosity prediction of hydrocarbon binary mixture using an artificial neural network-group contribution method

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Sara Nanvakenari ◽  
Mitra Ghasemi ◽  
Kamyar Movagharnejad
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Binary Mixture ◽  
Back Propagation ◽  
Training Algorithm ◽  
Group Contribution Method ◽  
Levenberg Marquardt ◽  
Hidden Layer

Abstract In this study, the viscosity of hydrocarbon binary mixtures has been predicted with an artificial neural network and a group contribution method (ANN-GCM) by utilizing various training algorithm including Scaled Conjugate Gradient (SCG), Levenberg-Marquardt (LM), Resilient back Propagation (RP), and Gradient Descent with variable learning rate back propagation (GDX). Moreover, different transfer functions such as Tan-sigmoid (tansig), Log-sigmoid (logsig), and purelin were investigated in hidden and output layer and their effects on network precision were estimated. Accordingly, 796 experimental data points of viscosity of hydrocarbon binary mixture were collected from the literature for a wide range of operating parameters. The temperature, pressure, mole fraction, molecular weight, and structural group of the system were selected as the independent input parameters. The statistical analysis results with R 2 = 0.99 revealed a small value for Average absolute relative deviation (AARD) of 1.288 and Mean square error (MSE) of 0.001018 by comparing the ANN predicted data with experimental data. Neural network configuration was also optimized. Based on the results, the network with one hidden layer and 27 neurons with the Levenberg-Marquardt training algorithm and tansig transfer function for hidden layer along with purelin transfer function for output layer constituted the best network structure. Further, the weights and bias were optimized to minimize the error. Then, the obtained results of the present study were compared with the data from some previous methods. The results suggested that this work can predict the viscosity of hydrocarbon binary mixture with better AARD. In general, the results indicated that combining ANN and GCM model is capable to predict the viscosity of hydrocarbon binary mixtures with a good accuracy.

scholarly journals Thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network

2020 ◽  
Vol 15 ◽  
pp. 155892501990083
Author(s):  
Xintong Li ◽  
Honglian Cong ◽  
Zhe Gao ◽  
Zhijia Dong
Keyword(s):  
Neural Network ◽  
Water Vapor ◽  
Thermal Resistance ◽  
Resistance Test ◽  
Training Algorithm ◽  
Backpropagation Algorithm ◽  
Optimal Network ◽  
Resistance Model ◽  
Hidden Layer ◽  
The Relationship

In this article, thermal resistance test and water vapor resistance test were experimented to obtain data of heat and humidity performance. Canonical correlation analysis was used on determining influence of basic fabric parameters on heat and humidity performance. Thermal resistance model and water vapor resistance model were established with a three-layered feedforward-type neural network. For the generalization of the network and the difficulty of determining the optimal network structure, trainbr was chosen as training algorithm to find the relationship between input factors and output data. After training and verification, the number of hidden layer neurons in the thermal resistance model was 12, and the error reached 10−3. In the water vapor resistance model, the number of hidden layer neurons was 10, and the error reached 10−3.

scholarly journals Accuracy of the Neurons Number in the Hidden Layer of the Levenberg-Marquardt Algorithm

2019 ◽  
Vol 8 (4) ◽  
pp. 2349-2353
Keyword(s):  
Mixed Method ◽  
Learning Method ◽  
Training Algorithm ◽  
Anova Analysis ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Input Layer ◽  
Admission System ◽  
Hidden Layer ◽  
New Student

Backpropagation, as a learning method in artificial neural networks, is widely used to solve problems in various fields of life, including education. In this field, backpropagation is used to predict the validity of questions, student achievement, and the new student admission system. The performance of the training algorithm is said to be optimal can be seen from the error (MSE) generated by the network. The smaller the error produced, the more optimal the performance of the algorithm. Based on previous studies, we got information that the most optimal training algorithm based on the smallest error was Levenberg–Marquardt with an average MSE = 0.001 in the 5-10-1 model with a level of α = 5%. In this study, we test the Levenberg-Marquardt algorithm on 8, 12, 14, 16, 19 neurons in hidden layers. This algorithm is tested at the learning rate (LR) = 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1. This study uses mixed-method, namely development with quantitative and qualitative testing using ANOVA and correlation analysis. The research uses random data with ten neurons in the input layer and one neuron in the output layer. Based on ANOVA analysis of the five variations in the number of neurons in the hidden layer, the results showed that with α = 5% as previous research, the Levenberg–Marquardt algorithm produced the smallest MSE of 0.00019584038 ± 0.000239300998. The number of neurons in the hidden layer that reaches this MSE is 16 neurons at the level of LR = 0.8.

Adaptive Power-Saving Mechanism for VoIP Over WiMAX Based on Artificial Neural Network

2019 ◽  
pp. 158-177
Author(s):  
Tamer Emara
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Saving ◽  
Ieee 802.16 ◽  
Feed Forward Neural Network ◽  
Artificial Neural ◽  
Adaptive Power ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

The IEEE 802.16 system offers power-saving class type II as a power-saving algorithm for real-time services such as voice over internet protocol (VoIP) service. However, it doesn't take into account the silent periods of VoIP conversation. This chapter proposes a power conservation algorithm based on artificial neural network (ANN-VPSM) that can be applied to VoIP service over WiMAX systems. Artificial intelligent model using feed forward neural network with a single hidden layer has been developed to predict the mutual silent period that used to determine the sleep period for power saving class mode in IEEE 802.16. From the implication of the findings, ANN-VPSM reduces the power consumption during VoIP calls with respect to the quality of services (QoS). Experimental results depict the significant advantages of ANN-VPSM in terms of power saving and quality-of-service (QoS). It shows the power consumed in the mobile station can be reduced up to 3.7% with respect to VoIP quality.

scholarly journals Feed Forward Neural Network Modeling for Rainfall Prediction

2018 ◽  
Vol 73 ◽  
pp. 05017
Author(s):  
Yasin Hasbi ◽  
Warsito Budi ◽  
Santoso Rukun
Keyword(s):  
Neural Network ◽  
Autocorrelation Function ◽  
Seasonal Pattern ◽  
Optimization Method ◽  
Rainfall Data ◽  
Neural Network Modeling ◽  
Least Mean Square ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Hidden Layer

Prediction of rainfall data by using Feed Forward Neural Network (FFNN) model is proposed. FFNN is a class of neural network which has three layers for processing. In time series prediction, including in case of rainfall data, the input layer is the past values of the same series up to certain lag and the output layer is the current value. Beside a few lagged times, the seasonal pattern also considered as an important aspect of choosing the potential input. The autocorrelation function and partial autocorrelation function patterns are used as aid of selecting the input. In the second layer called hidden layer, the logistic sigmoid is used as activation function because of the monotonic and differentiable. Processing is done by the weighted summing of the input variables and transfer process in the hidden layer. Backpropagation algorithm is applied in the training process. Some gradient based optimization methods are used to obtain the connection weights of FFNN model. The prediction is the output resulting of the process in the last layer. In each optimization method, the looping process is performed several times in order to get the most suitable result in various composition of separating data. The best one is chosen by the least mean square error (MSE) criteria. The least of in-sample and out-sample predictions from the repeating results been the base of choosing the best optimization method. In this study, the model is applied in the ten-daily rainfall data of ZOM 136 Cokrotulung Klaten. Simulation results give a consecution that the more complex architecture is not guarantee the better prediction.

ANN Modeling of Motional Resistance for Micro Disk Resonator

2020 ◽  
pp. 199-216
Author(s):  
Manjula A. Sutagundar ◽  
Basavaprabhu G. Sheeparamatti ◽  
Dakshayani S. Jangamshetti
Keyword(s):  
Neural Network ◽  
Resonance Frequency ◽  
Simulation Software ◽  
Equivalent Model ◽  
Ann Model ◽  
Feed Forward Neural Network ◽  
Disk Resonator ◽  
Motional Resistance ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

This article describes how modeling is an integral part of design and development of any system that provides the theoretical characterization of the system and helps in understanding the relations between various parameters of the system, before the system is developed. The capability of an Artificial Neural Network (ANN) to model the complex relations between a set of inputs and outputs is exploited to model the motional resistance and resonance frequency for a contour mode disk resonator. The solution was to develop a multilayer feed forward neural network. The data set required to train the ANN is obtained by developing an electrical equivalent model and through the MEMS simulation software Coventorware. The network is trained using a Levenberg Marquardt algorithm. The number of hidden layers and the number of neurons in each hidden layer is optimized using a genetic algorithm. The ANN model developed an efficient model of the motional resistance and resonance frequency of the disk resonator. The ANN output is compared with the output of an electrical equivalent model and a reported fabricated structure.

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