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 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.                                  

HUMAN DRIVING SKILL FOR HUMAN ADAPTIVE MECHATRONICS APPLICATIONS BY USING NEURAL NETWORK SYSTEM

2015 ◽  
Vol 76 (7) ◽  
Author(s):  
Mohamad Hafis Izran Ishak ◽  
Mazleenda Mazni ◽  
Amirah 'Aisha Badrul Hisham
Keyword(s):  
Neural Network ◽  
Network System ◽  
Critical Stage ◽  
Human Skill ◽  
Driving Skill ◽  
Neural Network System ◽  
Human Need ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
The Relationship

The existence of the new improvement system for Human Machine System (HMS) is called as Human Adaptive Mechatronic (HAM) system. The main difference between these two systems is the relationship between human and machine in the system. HMS is one way relationship between human and machine while HAM is a two way relationship between human and machine. In HAM, not only human need to adapt the characteristics of machine but the machine also has to learn on human characteristics. As a part of mechatronics system, HAM has an ability to adapt with human skill to improve the performance of machine. Driving a car is one of the examples of application where HAM can be applied. One of the important elements in HAM is the quantification of human skill. Therefore, this project proposed a method to quantify the driving skill by using Artificial Neural Network (ANN) system. Feedforward neural network is used to create a multilayer neural network and five models of network were designed and tested using MATLAB Simulink software. Then, the best model from five models is chosen and compared with other method of quantification skill for verification. Based on results, the critical stage in designing the network of the system is to set the number of neurons in the hidden layer that affects an accuracy of the outputs.

scholarly journals New S-box calculation approach for Rijndael-AES based on an artificial neural network

2017 ◽  
Vol 6 (2) ◽  
pp. E1-1-E1-21
Author(s):  
Jaime David Rios Arrañaga ◽  
◽  
Janneth Alejandra Salamanca Chavarin ◽  
Juan José Raygoza Panduro ◽  
Edwin Christian Becerra Alvarez ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Galois Field ◽  
Block Ciphers ◽  
Multiplicative Inverse ◽  
Symmetric Key ◽  
Artificial Neural ◽  
Hidden Layer ◽  
The Relationship

The S-box is a basic important component in symmetric key encryption, used in block ciphers to confuse or hide the relationship between the plaintext and the ciphertext. In this paper a way to develop the transformation of an input of the S-box specified in AES encryption system through an artificial neural network and the multiplicative inverse in Galois Field is presented. With this implementation more security is achieved since the values of the S-box remain hidden and the inverse table serves as a distractor since it would appear to be the complete S-box. This is implemented on MATLAB and HSPICE using a network of perceptron neurons with a hidden layer and null error.

Evaluation of Thermal Insulation Properties of Fibrous Mineral Fine Powders

2010 ◽  
Vol 178 ◽  
pp. 339-343
Author(s):  
Fei Wang ◽  
Jin Sheng Liang ◽  
Chong Yan Ren ◽  
Qing Guo Tang
Keyword(s):  
Experimental Data ◽  
Thermal Resistance ◽  
Thermal Insulation ◽  
Thermal Conduction ◽  
Fibrous Materials ◽  
Fine Powders ◽  
Resistance Model ◽  
Fibrous Mineral ◽  
The Relationship ◽  
Sepiolite Nanofibers

The equivalent thermal resistance model of sepiolite mineral nanofibers has been presented in this paper to predict the thermal insulation properties of fibrous mineral fine powders. The model was based on the correlation between thermal conduction and gas & solid conduction in the fibrous system. According to the analysis about the process of heat transfer in sepiolite nanofibers, the total thermal conduction can be described as the synergism of the solid thermal conduction and the gaseous thermal conduction. From the equivalent thermal resistance model of fibrous materials in the accumulative condition, it can be seen that the thermal conduction of fibrous mineral fine powders can be evaluated by the relationship between bulk density and thermal conduction of sepiolite nanofibers. Comparing the theoretical values with experimental data obtained from thermal conduction instrument, it was found that the theoretical values corresponded well with experimental data.

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 Kombinasi Algoritma Backpropagation Neural Network dengan Gravitational Search Algorithm Dalam Meningkatkan Akurasi

2021 ◽  
Vol 5 (1) ◽  
pp. 90
Author(s):  
Miftahul Falah ◽  
Dian Palupi Rini ◽  
Iwan Pahendra
Keyword(s):  
Neural Network ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Disease Prediction ◽  
Backpropagation Algorithm ◽  
Cancer Data ◽  
Long Time ◽  
Gravitational Search ◽  
Artificial Neural ◽  
Hidden Layer

Predicting disease is usually done based on the experience and knowledge of the doctor. Diagnosis of such a disease is traditionally less effective. The development of medical diagnosis based on machine learning in terms of disease prediction provides a more accurate diagnosis than the traditional way. In terms of predicting disease can use artificial neural networks. The artificial neural network consists of various algorithms, one of which is the Backpropagation Algorithm. In this paper it is proposed that disease prediction systems use the Backpropagation algorithm. Backpropagation algorithms are often used in disease prediction, but the Backpropagation algorithm has a slight drawback that tends to take a long time in obtaining optimum accuracy values. Therefore, a combination of algorithms can overcome the shortcomings of the Backpropagation algorithm by using the success of the Gravitational Search Algorithm (GSA) algorithm, which can overcome the slow convergence and local minimum problems contained in the Backpropagation algorithm. So the authors propose to combine the Backpropagation algorithm using the Gravitational Search Algorithm (GSA) in hopes of improving accuracy results better than using only the Backpropagation algorithm. The results resulted in a higher level of accuracy with the same number of iterations than using Backpropagation only. Can be seen in the first trial of breast cancer data with parameters namely hidden layer 5, learning rate of 2 and iteration as much as 5000 resulting in accuracy of 99.3 % with error 0.7% on Backpropagation Algorithm, while in combination BP & GSA got accuracy of 99.68 % with error of 0.32%.

scholarly journals Prediction of penetration rate in drilling operations: a comparative study of three neural network forecast methods

2021 ◽  
Vol 11 (2) ◽  
pp. 805-818
Author(s):  
Ehsan Brenjkar ◽  
Ebrahim Biniaz Delijani ◽  
Kasra Karroubi
Keyword(s):  
Neural Network ◽  
Drilling Process ◽  
Backpropagation Algorithm ◽  
Nsga Ii ◽  
Time Saving ◽  
Selection Step ◽  
Drilling Operations ◽  
Input Variables ◽  
Southwestern Iran ◽  
The Relationship

AbstractOptimizing purposes of the drilling process include reduction in time, saving costs, and increasing efficiency, which requires optimization of controllable variables and variables affecting the drilling process. Drilling optimization is directly related to maximizing the rate of penetration (ROP). However, estimation of ROP is difficult due to the complexity of the relationship between the variables affecting the drilling process. The main goal of this study is to develop three computational intelligence (CI)-based models including multilayer perceptron neural network optimized by backpropagation algorithm (BP-MLPNN), cascade-forward neural network optimized by backpropagation algorithm, and radial basis function neural network optimized by biogeography-based optimization algorithm (BBO-RBFNN) to estimate ROP. Also, in order to broaden the comparisons, some conventional ROP models from the literature were employed. The required data were collected from the well log unit and the final drilling reports of four drilled wells in two different oil fields in southwestern Iran. Firstly, all data were preprocessed to remove outliers; then the overall noises of the data were reduced by implementing Savitzky–Golay smoothing filter. In the next stage, nine input variables were selected during a feature selection step by combining the BP-MLPNN and NSGA-II algorithm. The results of this study showed that developed CI-based models more accurate than conventional ROP models. Also, a survey of statistical indices and graphical error tools proved that BBO-RBFNN model has the highest performance to predict ROP with values of APRE, AAPRE, RMSE and R2 equal to  − 0.603, 5.531, 0.490 and 0.948, respectively.

Optimization of the Composite Truncated Cone Structure Layers under Buckling Load

2012 ◽  
Vol 225 ◽  
pp. 144-149
Author(s):  
Hadi Samareh Salavati Pour ◽  
Mojtaba Sadighi ◽  
Abdolvahed Kami
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Back Propagation ◽  
Activation Function ◽  
Buckling Load ◽  
Training Data ◽  
Training Algorithm ◽  
Cone Structure ◽  
Truncated Cone ◽  
Hidden Layer

The orientation of fibers in the layers is an important factor that must be obtained in order to predict how well the finished composite product will perform under real-world working conditions. In this research, a five-layer glass-epoxy composite truncated cone structure under buckling load was considered. The simulation of the structure was done utilizing finite element method and was confirmed comparing with the published experimental results. Then the effect of different orientation of fibers on the buckling load was considered. For this, a computer programing was developed to compute the buckling load for different orientations of fibers in each layer. These orientations were produced randomly with the delicacy of 15 degrees. Finally, neural network and genetic algorithm methods were utilized to obtain the optimum orientations of fibers in each layer using the training data obtained from finite element simulation. There are many parameters such as the number of hidden layers, the number of neurons in each hidden layer, the training algorithm, the activation function and so on which must be specified properly in development of a neural network model. The number of hidden layers and number of neurons in each layer was obtained by try and error method. In this study, multilayer back-propagation (BP) neural network with the Levenberg-Marquardt training algorithm (trainlm) was used. Finally, the results showed that the truncated cone with optimum layers withstand considerably more buckling load.

scholarly journals Deep Autoencoder-Based Image Compression using Multi-Layer Perceptrons

2020 ◽  
Vol 9 (6) ◽  
pp. 1-6
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Backpropagation Algorithm ◽  
Feed Forward Neural Network ◽  
The Neural Network ◽  
Proposed Model ◽  
Hidden Layer ◽  
Preprocessing Technique ◽  
Block Sizes ◽  
Normalization Process

The Artificial Neural Network is one of the heavily used alternatives for solving complex problems in machine learning and deep learning. In this research, a deep autoencoder-based multi-layer feed-forward neural network has been proposed to achieve image compression. The proposed neural network splits down a large image into small blocks and each block applies the normalization process as the preprocessing technique. Since this is an autoencoder-based neural network, each normalized block of pixels has been initialized as the input and the output of the neural network. The training process of the proposed network has been done for various block sizes and different saving percentages of various kinds of images by using the backpropagation algorithm. The output of the middle-hidden layer will be the compressed representation for each block of the image. The proposed model has been implemented using Python, Keras, and Tensorflow backend.

scholarly journals AN EFFICIENT FUZZY NEURAL NETWORK TRAINING MODEL FOR SUPERVISED PATTERN CLASSIFICATION SYSTEM

2016 ◽  
Vol 12 (11) ◽  
pp. 4488-4499
Author(s):  
Manjula Devi ◽  
S.J. Suji Prasad ◽  
Sagana C
Keyword(s):  
Neural Network ◽  
Training Model ◽  
Classification Problem ◽  
Training Algorithm ◽  
Accuracy Rate ◽  
Training Time ◽  
Fast Training ◽  
Fuzzy Neural ◽  
Benchmark Datasets ◽  
Hidden Layer

Among the existing NN architectures, Multilayer Feedforward Neural Network (MFNN) with single hidden layer architecture has been scrutinized thoroughly as best for solving nonlinear classification problem. The training time is consumed more for very huge training datasets in the MFNN training phase. In order to reduce the training time, a simple and fast training algorithm called Exponential Adaptive Skipping Training (EAST) Algorithm was presented that improves the training speed by significantly reducing the total number of training input samples consumed by MFNN for training at every single epoch. Although the training performance of EAST achieves faster, it still lacks in the accuracy rate due to high skipping factor. In order to improve the accuracy rate of the training algorithm, Hybrid system has been suggested in which the neural network is trained with the fuzzified data. In this paper, a z-Score Fuzzy Exponential Adaptive Skipping Training (z-FEAST) algorithm is proposed which is based on the fuzzification of EAST. The evaluation of the proposed z-FEAST algorithm is demonstrated effectively using the benchmark datasets - Iris, Waveform, Heart Disease and Breast Cancer for different learning rate. Simulation study proved that z-FEAST training algorithm improves the accuracy rate.

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