Using an artificial neural network for the evaluation of the parameters controlling PVA/chitosan electrospun nanofibers diameter

e-Polymers ◽  
2015 ◽  
Vol 15 (2) ◽  
pp. 127-138 ◽  
Author(s):  
Mohammad Ali Karimi ◽  
Pouran Pourhakkak ◽  
Mahdi Adabi ◽  
Saman Firoozi ◽  
Mohsen Adabi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Three Dimensional ◽  
Base Material ◽  
Electrospun Nanofibers ◽  
Poly Vinyl Alcohol ◽  
Nanofiber Diameter ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

AbstractThe purpose of this study was to investigate the validity of an artificial neural network (ANN) method in the prediction of nanofiber diameter to assess the parameters involved in controlling fiber form and thickness. A mixture of polymers including poly(vinyl alcohol) (PVA) and chitosan (CS) at different ratios was chosen as the nanofiber base material. The various samples of nanofibers were fabricated as training and testing datasets for ANN modeling. Different networks of ANN were designed to achieve the purposes of this study. The best network had three hidden layers with 8, 16 and 5 nodes in each layer, respectively. The mean squared error and correlation coefficient between the observed and the predicted diameter of the fibers in the selected model were equal to 0.09008 and 0.93866, respectively, proving the efficacy of the ANN technique in the prediction process. Finally, three-dimensional graphs of the electrospinning parameters involved and nanofiber diameter were plotted to scrutinize the implications.

scholarly journals Artificial neural network for solving multi-parameter optimization problems

2021 ◽  
Vol 2092 (1) ◽  
pp. 012013
Author(s):  
Krivorotko Olga ◽  
Liu Shuang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Problems ◽  
Three Dimensional ◽  
Test Functions ◽  
Backpropagation Method ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Quality Of Approximation ◽  
And Function

Abstract An artificial neural network (ANN) is a mathematical or computational model that simulates the structure and function of biological neural networks used to evaluate or approximate functions at given points. After developing the training algorithm, the resulting model will be used to solve image recognition problems, control problems, optimization, etc. In the process of ANN training, the algorithm of backpropagation is used in the case of convex optimization functions. The article is analyzed test functions for experiments and also study the effect of the number of ANN layers on the quality of approximation in cases one-, two- and three-dimensional. The backpropagation method is improved during the experiments with the help of adaptive gradient, as a result of which more accurate approximations of the functions are obtained. This article also presents the numerical results of test functions.

scholarly journals An Artificial Neural Network Based Solution Scheme for Periodic Computational Homogenization of Electrostatic Problems

2019 ◽  
Vol 24 (2) ◽  
pp. 40 ◽  
Author(s):  
Felix Selim Göküzüm ◽  
Lu Trong Khiem Nguyen ◽  
Marc-André Keip
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Trial Function ◽  
A Priori ◽  
Three Dimensional ◽  
Internal Parameters ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Core Idea ◽  
Electrostatic Problems

The present work addresses a solution algorithm for homogenization problems based on an artificial neural network (ANN) discretization. The core idea is the construction of trial functions through ANNs that fulfill a priori the periodic boundary conditions of the microscopic problem. A global potential serves as an objective function, which by construction of the trial function can be optimized without constraints. The aim of the new approach is to reduce the number of unknowns as ANNs are able to fit complicated functions with a relatively small number of internal parameters. We investigate the viability of the scheme on the basis of one-, two- and three-dimensional microstructure problems. Further, global and piecewise-defined approaches for constructing the trial function are discussed and compared to finite element (FE) and fast Fourier transform (FFT) based simulations.

scholarly journals Prediction of rubber vulcanization using an artificial neural network

2021 ◽  
Vol 75 (5) ◽  
pp. 277-283
Author(s):  
Jelena Lubura ◽  
Predrag Kojic ◽  
Jelena Pavlicevic ◽  
Bojana Ikonic ◽  
Radovan Omorjan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Large Scale ◽  
Mean Squared Error ◽  
Percentage Error ◽  
Dense Layer ◽  
Ann Model ◽  
Different Temperatures ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Determination of rubber rheological properties is indispensable in order to conduct efficient vulcanization process in rubber industry. The main goal of this study was development of an advanced artificial neural network (ANN) for quick and accurate vulcanization data prediction of commercially available rubber gum for tire production. The ANN was developed by using the platform for large-scale machine learning TensorFlow with the Sequential Keras-Dense layer model, in a Python framework. The ANN was trained and validated on previously determined experimental data of torque on time at five different temperatures, in the range from 140 to 180 oC, with a step of 10 oC. The activation functions, ReLU, Sigmoid and Softplus, were used to minimize error, where the ANN model with Softplus showed the most accurate predictions. Numbers of neurons and layers were varied, where the ANN with two layers and 20 neurons in each layer showed the most valid results. The proposed ANN was trained at temperatures of 140, 160 and 180 oC and used to predict the torque dependence on time for two test temperatures (150 and 170 oC). The obtained solutions were confirmed as accurate predictions, showing the mean absolute percentage error (MAPE) and mean squared error (MSE) values were less than 1.99 % and 0.032 dN2 m2, respectively.

Artificial Neural Network for Markov Chaining of Rainfall Over India

2020 ◽  
Vol 7 (3) ◽  
pp. 71-84
Author(s):  
Kavita Pabreja
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Multiple Linear Regression Model ◽  
Monthly Rainfall ◽  
Series Data ◽  
Rainfall Forecasting ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Rainfall forecasting plays a significant role in water management for agriculture in a country like India where the economy depends heavily upon agriculture. In this paper, a feed forward artificial neural network (ANN) and a multiple linear regression model has been utilized for lagged time series data of monthly rainfall. The data for 23 years from 1990 to 2012 over Indian region has been used in this study. Convincing values of root mean squared error between actual monthly rainfall and that predicted by ANN has been found. It has been found that during monsoon months, rainfall of every n+3rd month can be predicted using last three months' (n, n+1, n+2) rainfall data with an excellent correlation coefficient that is more than 0.9 between actual and predicted rainfall. The probabilities of dry seasonal month, wet seasonal month for monsoon and non-monsoon months have been found.

scholarly journals Prediksi Inflasi Indonesia Memakai Model ARIMA dan Artificial Neural Network

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Wahyudin S
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Analysis Of Variance ◽  
Mean Squared Error ◽  
Statistics Analysis ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Inflasi merupakan indikator makro ekonomi yang sangat penting. Berbagai macam metoda prediksi inflasi Indonesia telah dipublikasikan. Namun pencarian metoda prediksi inflasi yang lebih akurat masih menjadi topik menarik. Pada penulisan ini diusulkan sebuah metoda baru untuk prediksi inflasi memakai model ARIMA dan Artificial Neural Network (ANN). Data inflasi yang digunakan adalah data inflasi bulanan year-on-year dari tahun 2010 sampai dengan tahun 2018 yang diterbitkan oleh Badan Pusat Statistik (BPS). Pertama dibuat 2 model ARIMA yaitu model ARIMA tanpa siklus tahunan dan dengan siklus tahunan. Prosedur standar dan diagostics test telah dilakukan antara lain: summary of statistics, analysis of variance (ANOVA), significance of coefficients test, residuals normality, heterocesdacity, dan stability. Dari hasil perbandingan kinerja memakai Root Mean Squared Error (RMSE) diperoleh bahwa model ARIMA dengan siklus tahunan lebih baik. Model tersebut berupa model ARIMA (2,1,0) (2,0,0) [12]. Kemudian, untuk meningkatkan kinerja prediksi inflasi, ANN telah dibuat berbasis model ARIMA tersebut. Model ANN memakai satu hidden layer dan dua neuron. Hasil pengujian menunjukkan bahwa model ANN menghasilkan RMSE yang lebih kecil daripada model ARIMA (2,1,0) (2,0,0) [12]. Hal ini kemungkinan disebabkan oleh kemampuan mengolah hubungan nonlinear antara variabel target dan variabel penjelas.

scholarly journals Application of artificial neural network to predict amount of carried weight of cargo train in rail transportation system

2020 ◽  
Vol 9 (3) ◽  
pp. 480
Author(s):  
Siti Nasuha Zubir ◽  
S. Sarifah Radiah Shariff ◽  
Siti Meriam Zahari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Percentage Error ◽  
Training Algorithms ◽  
Training Algorithm ◽  
Error Measures ◽  
Train Services ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<span lang="EN-US">Derailments of cargo have frequently occurred in Malaysian train services during the last decade. Many factors contribute to this incident, especially its total amount of carried weight. It is found that severe derailments cause damage to both lives and properties every year. If the amount of carried weight of cargo train could be accurately forecasted in advance, then its detrimental effect could be greatly minimized. This paper presents the application of Artificial Neural Network (ANN) to predict the amount of carried weight of cargo train, with KTMB used as the study case. As there are many types of cargo being carried by KTMB, this study focuses only on cement that being carried in twelve (12) different routes. In this study, Artificial Neural Network (ANN) has been incorporated for developing a predictive model with three (3) different training algorithms, Levenberg-Marquardt (LM), Quick Propagation (QP) and Conjugate Gradient Descent (CGD). The best training algorithm is selected to predict the amount of carried weight by comparing the error measures of all the training algorithm which are Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The obtained results indicated that the ANN technique is suitable for predicting the amount of carried weight.</span>

Artificial Neural Network for Markov Chaining of Rainfall Over India

2022 ◽  
pp. 1130-1145
Author(s):  
Kavita Pabreja
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Multiple Linear Regression Model ◽  
Monthly Rainfall ◽  
Series Data ◽  
Rainfall Forecasting ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Rainfall forecasting plays a significant role in water management for agriculture in a country like India where the economy depends heavily upon agriculture. In this paper, a feed forward artificial neural network (ANN) and a multiple linear regression model has been utilized for lagged time series data of monthly rainfall. The data for 23 years from 1990 to 2012 over Indian region has been used in this study. Convincing values of root mean squared error between actual monthly rainfall and that predicted by ANN has been found. It has been found that during monsoon months, rainfall of every n+3rd month can be predicted using last three months' (n, n+1, n+2) rainfall data with an excellent correlation coefficient that is more than 0.9 between actual and predicted rainfall. The probabilities of dry seasonal month, wet seasonal month for monsoon and non-monsoon months have been found.

Predicting the Air Permeability of Ultrafine Glass Fiber Felts: A Comparison of Artificial Neural Network, Linear Fitting, and Polynomial Fitting

2021 ◽  
Vol 8 (2) ◽  
pp. 9-17
Author(s):  
Fei Wang ◽  
Zhaofeng Chen ◽  
Cao Wu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Glass Fiber ◽  
Mean Squared Error ◽  
Air Permeability ◽  
Ann Model ◽  
Polynomial Fitting ◽  
Linear Fitting ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this study, the air permeability of ultrafine glass fiber felts (UGFFs) as a function of bulk density and thickness was predicted by three analysis methods including linear fitting, polynomial fitting, and an artificial neural network (ANN). A 36-set database was obtained by the measurements of samples produced by the flame blowing process. It was shown that the ANN structure with six neurons in the hidden layer was optimal. The ANN model showed much better quality of predicting the permeation rate compared with linear fitting and polynomial fitting, which was evaluated by three important parameters, namely mean relative error (MRE), mean squared error (MSE), and correlation coefficient (R). The prediction diagrams applying the ANN model also matched the theoretical analysis very well, which verified the advantages and practicability of ANN.

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