scholarly journals PREDICTION OF THE SIZE OF NANOPARTICLES AND MICROSPORE SURFACE AREA USING ARTIFICIAL NEURAL NETWORK

2018 ◽  
Vol 1 (1) ◽  
pp. 65
Author(s):  
Dženana Sarajlić ◽  
Layla Abdel-Ilah ◽  
Adnan Fojnica ◽  
Ahmed Osmanović
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Surface Area ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
Experimental Values

This paper presents development of Artificial Neural Network (ANN) for prediction of the size of nanoparticles (NP) and microspore surface area (MSA). Developed neural network architecture has the following three inputs: the concentration of the biodegradable polymer in the organic phase, surfactant concentration in the aqueous phase and the homogenizing pressure. Two-layer feedforward network with a sigmoid transfer function in the hidden layer and a linear transfer function in the output layer is trained, using Levenberg-Marquardt training algorithm. For training of this network, as well as for subsequent validation, 36 samples were used. From 36 samples which were used for subsequent validation in this ANN, 80,5% of them had highest accuracy while 19,5% of output data had insignificant differences comparing to experimental values.

ARTIFICIAL NEURAL NETWORK (ANN)-BASED PREDICTIVE TOOL FOR ESTIMATING LIGHTNING DAMAGE IN COMPOSITES

2021 ◽  
Author(s):  
DEVIN NIELSEN ◽  
TYLER LOTT ◽  
SOM DUTTA ◽  
JUHYEONG LEE
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Back Propagation ◽  
Training Dataset ◽  
Predictive Tool ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

In this study, three artificial neural network (ANN) models are developed with back propagation (BP) optimization algorithms to predict various lightning damage modes in carbon/epoxy laminates. The proposed ANN models use three input variables associated with lightning waveform parameters (i.e., the peak current amplitude, rising time, and decaying time) to predict fiber damage, matrix damage, and through-thickness damage in the composites. The data used for training and testing the networks was actual lightning damage data collected from peer-reviewed published literature. Various BP training algorithms and network architecture configurations (i.e., data splitting, the number of neurons in a hidden layer, and the number of hidden layers) have been tested to improve the performance of the neural networks. Among the various BP algorithms considered, the Bayesian regularization back propagation (BRBP) showed the overall best performance in lightning damage prediction. When using the BRBP algorithm, as expected, the greater the fraction of the collected data that is allocated to the training dataset, the better the network is trained. In addition, the optimal ANN architecture was found to have a single hidden layer with 20 neurons. The ANN models proposed in this work may prove useful in preliminary assessments of lightning damage and reduce the number of expensive experimental lightning tests.

Simultaneous Quantitative Analysis of Clorsulon and Ivermectin in a Veterinary Formulation by Artificial Neural Network

2008 ◽  
Vol 59 (10) ◽  
Author(s):  
Gozde Pektas ◽  
Erdal Dinc ◽  
Dumitru Baleanu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Back Propagation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Simultaneaous spectrophotometric determination of clorsulon (CLO) and invermectin (IVE) in commercial veterinary formulation was performed by using the artificial neural network (ANN) based on the back propagation algorithm. In order to find the optimal ANN model various topogical networks were tested by using different hidden layers. A logsig input layer, a hidden layer of neurons using the logsig transfer function and an output layer of two neurons with purelin transfer function was found suitable for basic configuration for ANN model. A calibration set consisting of CLO and IVE in calibration set was prepared in the concentration range of 1-23 �g/mL and 1-14 �g/mL, repectively. This calibration set contains 36 different synthetic mixtures. A prediction set was prepared in order to evaluate the recovery of the investigated approach ANN chemometric calibration was applied to the simultaneous analysis of CLO and IVE in compounds in a commercial veterinary formulation. The experimental results indicate that the proposed method is appropriate for the routine quality control of the above mentioned active compounds.

scholarly journals PERAMALAN JUMLAH PERMINTAAN UDANG BEKU PND MENGGUNAKAN METODE JARINGAN SYARAF TIRUAN (JST) BACKPROPAGATION

2017 ◽  
Vol 11 (1) ◽  
pp. 17 ◽  
Author(s):  
Iid Mufidah ◽  
Sony Suwasono ◽  
Yuli Wibowo ◽  
Deddy Wirawan Soedibyo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Series Data ◽  
Architecture Design ◽  
Neural Network Architecture ◽  
Output Layer ◽  
Artificial Neural Network Architecture ◽  
Artificial Neural ◽  
Hidden Layer

Forecasting is the art or science to estimate how many needs will come in order to meet the demand for goods or services, often based on historical time series data. The growing number of emerging companies in Indonesia today has created a very tight business competition in both services and products. Consumers choose the best service and high quality and low price. Consumer demand is always uncertain or varied in each subsequent period. The aim of this research was to determind the best backpropagation neural network architecture design and to predict the demand of frozen product of PND 26/30. This research used the method of Neural Network (ANN) and Processing ANN using MATLAB software. Implementation of ANN method in PT.XYZ using Backpropagation algorithm. Artificial neural network architecture used was 12 input layer, 1 output layer, and 12 hidden layer and activation function used tansig and purelin. Tansig for hidden layer and purelin for output layer. The best artificial neural network architecture design for product demand for PND 31/40 was a multi layer feedforward value of Mean Square Error (MSE) network training value of 0.01 with MAPE 3.35. The result of JST forecasting period 2017 were 960 MC, 637 MC, 572 MC, 993 MC, 1386 MC, 480 MC, 135 MC, 1209 MC, 1476 MC, 1029 MC, 290 MC, and 952 MC. Keywords: artificial neural network, PND 26/30, backpropagation, MSE, MAPE

scholarly journals Investigating the Electronic Coefficients in Ne-Hg Mixtures: Comparing a Monte Carlo and Artificial Neural Network Model

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Rafid Abbas Ali ◽  
Faten Sajet Mater ◽  
Asmaa Satar Jeeiad Al-Ragehey
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Network Architecture ◽  
Vapor Concentration ◽  
Neural Network Architecture ◽  
Marquardt Algorithm ◽  
Artificial Neural ◽  
New Applications

Electron coefficients such as drift velocity, ionization coefficient, mean electron energy and Townsend energy for different concentrations of Hg 0.1%, 1%, 10% and 50% in the Ne-Hg mixture at a reduced electric field were calculated using two approaches taking into account inelastic collisions: The Monte Carlo simulation, and an artificial neural network. The effect of Hg vapor concentration on the electron coefficients showed that insignificant additions of mercury atom impurities to Neon, starting from fractions of a percent, affect the characteristics of inelastic processes and discharge, respectively. The aim of this paper is to explore the new applications of neural networks. The Levenberg-Marquardt algorithm and artificial neural network architecture employed was presented in this work to calculate the electron coefficients for different concentrations of Hg in Ne-Hg mixtures. The artificial neural network has been trained with four models (M1, M2, M3, M4), and analysis of the regression between the values of an artificial neural network and Monte Carlo simulation indicates that the M2 output provided the best perfect correlation at 100 Epochs, and the output data obtained was closest to the target data required through using the different stages of artificial neural network development starting with design, training and testing.

Perbandingan Model Transfer Function Dan Model Neural Network Untuk Prediksi Banyak Kasus Demam Berdarah Di Kota Malang

2021 ◽  
Vol 11 (1) ◽  
pp. 1-9
Author(s):  
Nanta Sigit ◽  
Ida Ayu P K
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Model Transfer ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Model Transfer Function

ABSTRAK Kota Malang adalah salah satu kota yang dinyatakan sebagai daerah endemis demam berdarah. Pada tahun 2015, jumlah penderita demam berdarah sebanyak 1629 kasus dengan jumlah kematian 13 orang. Ada banyak faktor yang berkontribusi menyebabkan penyakit, begitu juga dengan penyakit demam berdarah. Faktor-faktor tersebut berasal dari individu sendiri maupun dari lingkungan. Beberapa faktor yang terkait dalam penularan demam berdarah antara lain kepadatan penduduk, mobilitas penduduk, kualitas perumahan dan sikap hidup. Sedangkan faktor yang dapat memicu terjadinya demam berdarah adalah faktor lingkungan yang termasuk di dalamnya perubahan suhu, kelembaban udara, dan curah hujan yang mengakibatkan nyamuk lebih sering bertelur dan virus dengue berkembang biak dengan cepat. Parasit dan pembawa penyakit (nyamuk) sangat peka terhadap faktor iklim, khususnya suhu, curah hujan, kelembaban, permukaan air, dan angin. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan suatu model yang  sesuai untuk peramalan demam berdarah dikota malang berdasarkan Transfer Function dan ANN. Data yang digunakan adalah Data demam berdarah tahun 2014 sampai 2019. Hasil penelitian menunjukkan bahwa nilai RMSE, MAPE, dan SMAPE yang terkecil dari kedua model tersebut adalah model Artificial Neural Network.   Kata Kunci : Artificial Neural Network (ANN), Transfer Function, dan Demam Berdarah

Perbandingan Model Transfer Function Dan Model Neural Network Untuk Prediksi Banyak Kasus Demam Berdarah Di Kota Malang

2021 ◽  
Vol 11 (1) ◽  
pp. 1-9
Author(s):  
Nanta Sigit ◽  
Ida Ayu P K
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Model Transfer ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Model Transfer Function

ABSTRAK Kota Malang adalah salah satu kota yang dinyatakan sebagai daerah endemis demam berdarah. Pada tahun 2015, jumlah penderita demam berdarah sebanyak 1629 kasus dengan jumlah kematian 13 orang. Ada banyak faktor yang berkontribusi menyebabkan penyakit, begitu juga dengan penyakit demam berdarah. Faktor-faktor tersebut berasal dari individu sendiri maupun dari lingkungan. Beberapa faktor yang terkait dalam penularan demam berdarah antara lain kepadatan penduduk, mobilitas penduduk, kualitas perumahan dan sikap hidup. Sedangkan faktor yang dapat memicu terjadinya demam berdarah adalah faktor lingkungan yang termasuk di dalamnya perubahan suhu, kelembaban udara, dan curah hujan yang mengakibatkan nyamuk lebih sering bertelur dan virus dengue berkembang biak dengan cepat. Parasit dan pembawa penyakit (nyamuk) sangat peka terhadap faktor iklim, khususnya suhu, curah hujan, kelembaban, permukaan air, dan angin. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan suatu model yang  sesuai untuk peramalan demam berdarah dikota malang berdasarkan Transfer Function dan ANN. Data yang digunakan adalah Data demam berdarah tahun 2014 sampai 2019. Hasil penelitian menunjukkan bahwa nilai RMSE, MAPE, dan SMAPE yang terkecil dari kedua model tersebut adalah model Artificial Neural Network. Kata Kunci : Artificial Neural Network (ANN), Transfer Function, dan Demam Berdarah

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