Reliability estimation from two types of accelerated testing data based on an artificial neural network supported Wiener process

2022 ◽  
Vol 417 ◽  
pp. 126757
Author(s):  
Di Liu ◽  
Shaoping Wang ◽  
Chao Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wiener Process ◽  
Reliability Estimation ◽  
Accelerated Testing ◽  
Testing Data ◽  
Artificial Neural

An artificial neural network-based critical values for multiple hypothesis testing: data-snooping case

Survey Review ◽  
2021 ◽  
pp. 1-16
Author(s):  
Vinicius Francisco Rofatto ◽  
Marcelo Tomio Matsuoka ◽  
Ivandro Klein ◽  
Maria Luísa Silva Bonimani ◽  
Bruno Póvoa Rodrigues ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hypothesis Testing ◽  
Multiple Hypothesis Testing ◽  
Critical Values ◽  
Data Snooping ◽  
Multiple Hypothesis ◽  
Testing Data ◽  
Artificial Neural

Predictive modeling of geometric shapes of different objects using image processing and an artificial neural network

2016 ◽  
Vol 231 (6) ◽  
pp. 1206-1216 ◽  
Author(s):  
Mustafa Ayyıldız ◽  
Kerim Çetinkaya
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Conjugate Gradient ◽  
Back Propagation ◽  
Neural Network Modeling ◽  
Back Propagation Algorithm ◽  
Geometric Shapes ◽  
Testing Data ◽  
Artificial Neural

In this study, an artificial neural network model was developed to predict the geometric shapes of different objects using image processing. These objects with various sizes and shapes (circle, square, triangle, and rectangle) were used for the experimental process. In order to extract the features of these geometric shapes, morphological features, including the area, perimeter, compactness, elongation, rectangularity, and roundness, were applied. For the artificial neural network modeling, the standard back-propagation algorithm was found to be the optimum choice for training the model. In the building of the network structure, five different learning algorithms were used: the Levenberg–Marquardt, the quasi-Newton back propagation, the scaled conjugate gradient, the resilient back propagation, and the conjugate gradient back propagation. The best result was obtained by 6-5-1 network architectures with single hidden layers for the geometric shapes. After artificial neural network training, the correlation coefficients ( R2) of the geometric shape values for training and testing data were very close to 1. Similarly, the root-mean-square error and mean error percentage values for the training and testing data were less than 0.9% and 0.004%, respectively. These results demonstrated that the artificial neural network is an admissible model for the estimation of geometric shapes using image processing.

Flow Stress Prediction of Ti-6Al-4V Alloy at Elevated Temperature Using Artificial Neural Network

2014 ◽  
Vol 612 ◽  
pp. 83-88 ◽  
Author(s):  
Nitin Kotkunde ◽  
Aditya Balu ◽  
Amit Kumar Gupta ◽  
Swadesh Kumar Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flow Stress ◽  
Strain Rate ◽  
Correlation Coefficient ◽  
Differential Evolution Algorithm ◽  
Training Data ◽  
Uniaxial Tensile ◽  
Testing Data ◽  
Artificial Neural

Flow stress during hot deformation depends mainly on the strain, strain rate and temperature, and shows a complex nonlinear relationship with them. In this work, experimental flow stress have been predicted for Ti-6Al-4V alloy using isothermal uniaxial tensile tests ranging from 323K to 673K at an interval of 50K and strain rates 10-5, 10-4, 10-3 and 10-2 s-1. Based on the input variables strain, strain rate and temperature, a back propagation neural network model has been developed to predict the flow stress as output. The whole experimental data is randomly divided in two parts: 90% data as training data and 10% data as testing data. The artificial neural network enhanced with differential evolution algorithm is successfully trained based on the training data and employed to predict the flow stress values for the testing data, which were compared with the experimental values. Correlation coefficient for training and testing data is found to be 0.9997 and 0.9985 respectively. Based on the correlation coefficient, it indicates that predicted flow stress by using artificial neural network is in good agreement with experimental results.

scholarly journals Implementasi Jaringan Syaraf Tiruan Recurrent Dengan Metode Pembelajaran Gradient Descent Adaptive Learning Rate Untuk Pendugaan Curah Hujan Berdasarkan Peubah Enso

2010 ◽  
Vol 1 (2) ◽  
pp. 418
Author(s):  
Afan Galih Salman ◽  
Yen Lina Prasetio
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Adaptive Learning ◽  
Gradient Descent ◽  
Learning Rate ◽  
Training Data ◽  
Testing Data ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Data Group

The use of technology of technology Artificial Neural Network (ANN) in prediction of rainfall can be done using the learning approach. ANN prediction accuracy measured by the coefficient of determination (R2) and Root Mean Square Error (RMSE).This research employ a recurrent optimized heuristic Artificial Neural Network (ANN) Recurrent Elman gradient descent adaptive learning rate approach using El-Nino Southern Oscilation (ENSO) variable, namely Wind, Southern Oscillation Index (SOI), Sea Surface Temperatur (SST) dan Outgoing Long Wave Radiation (OLR) to forecast regional monthly rainfall. The patterns of input data affect the performance of Recurrent Elman neural network in estimation process. The first data group that is 75% training data and 25% testing data produce the maximum R2 69.2% at leap 0 while the second data group that is 50% training data & 50% testing data produce the maximum R2 53.6%.at leap 0 Our result on leap 0 is better than leap 1,2 or 3. 

scholarly journals IMPLEMENTASI ALGORITMA BACKPROPAGATION UNTUK MEMPREDIKSI KELULUSAN MAHASISWA

2018 ◽  
Vol 5 (2) ◽  
pp. 169
Author(s):  
Muhammad Dedek Yalidhan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Information System ◽  
Prediction Accuracy ◽  
Confusion Matrix ◽  
The Other ◽  
Training Data ◽  
Backpropagation Algorithm ◽  
Testing Data ◽  
Artificial Neural

<p><em>Student’s graduation is one kind of the college accreditation elements by BAN-PT. Because of that. Information System is one of the department in STMIK Banjarbaru, there is no application has been implemented to predict imprecisely of student’s graduation time so far, which causes on time graduation percentage tend low every year. Therefore the accurate student’s graduation prediction can help the committe to choose the correct decisions in order to prevent the imprecisely of student’s graduation time. In this research, the backpropagation algorithm of artificial neural network will be implemented into the application with the output result as delayed and on time graduation. This reseach is using 318 data samples which the 70 % of it will be used as the training data and the other 30 % will be used as testing data. From the calculation of confusion matrix table’s the percentage of the prediction accuracy is 98.97 %.</em></p><p><em></em><em><strong>Keywords</strong>: student’s graduation, artificial neural network, backpropagation, confusion matrix</em></p><p><em></em><em>Kelulusan mahasiswa merupakan salah satu elemen dalam standar akreditasi perguruan tinggi oleh BAN-PT. Sistem Informasi adalah salah satu program studi yang ada di STMIK Banjarbaru, selama ini belum ada aplikasi yang diimplementasikan untuk memprediksi ketidaktepatan waktu kelulusan mahasiswanya yang menyebabkan angka kelulusan tepat waktu cenderung rendah setiap tahunnya. Oleh sebab itu, prediksi kelulusan mahasiswa yang akurat dapat membantu pihak Program Studi dalam mengambil keputusan-keputusan yang tepat untuk mencegah ketidaktepatan waktu kelulusan mahasiswanya. Pada penelitian ini, artificial neural network algoritma backpropagation diimplementasikan pada aplikasi yang dibuat dengan output lulus terlambat dan lulus tepat waktu. Penelitian ini menggunakan sebanyak 318 sampel data yang mana 70 % data digunakan sebagai data training dan 30 % data digunakan sebagai data testing. Dari hasil perhitungan tabel confusion matrix diperoleh persentase akurasi prediksi sebesar 98.97 %.</em></p><p><em></em><em><strong>Kata kunci</strong>: kelulusan mahasiswa, artificial neural network, backpropagation, confusion matrix</em></p>

scholarly journals ARTIFICIAL NEURAL NETWORK APPROACH FOR THE IDENTIFICATION OF CLOVE BUDS ORIGIN BASED ON METABOLITES COMPOSITION

2020 ◽  
Vol 60 (5) ◽  
pp. 440-447
Author(s):  
. Rustam ◽  
Agus Yodi Gunawan ◽  
Made Tri Ari Penia Kresnowati
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Network Approach ◽  
Neural Network Approach ◽  
Testing Data ◽  
Artificial Neural ◽  
Artificial Neural Network Approach

This paper examines the use of an artificial neural network approach in identifying the origin of clove buds based on metabolites composition. Generally, large data sets are critical for an accurate identification. Machine learning with large data sets lead to a precise identification based on origins. However, clove buds uses small data sets due to the lack of metabolites composition and their high cost of extraction. The results show that backpropagation and resilient propagation with one and two hidden layers identifies the clove buds origin accurately. The backpropagation with one hidden layer offers 99.91% and 99.47% for training and testing data sets, respectively. The resilient propagation with two hidden layers offers 99.96% and 97.89% accuracy for training and testing data sets, respectively.

scholarly journals Modeling of the 5G-Band Patch Antennas Using ANNs under the Uncertainty of the Geometrical Design Parameters Associated with the Manufacturing Process

Algorithms ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 7
Author(s):  
Piotr Górniak
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reflection Coefficient ◽  
Antenna Arrays ◽  
Patch Antenna ◽  
Manufacturing Process ◽  
Design Parameters ◽  
Patch Antennas ◽  
Testing Data ◽  
Artificial Neural

In the paper, the author deals with modeling the stochastic behavior of ordinary patch antennas in terms of the mean and standard deviation of their reflection coefficient |S11| under the geometrical uncertainty associated with their manufacturing process. The Artificial Neural Network is used to model the stochastic reflection coefficient of the antennas. The Polynomial Chaos Expansion and FDTD computations are used to obtain the training and testing data for the Artificial Neural Network. For the first time, the author uses his analytical transformations to reduce the required number of highly time-consuming FDTD simulations for a given set of nominal values of the design parameters of the ordinary patch antenna. An analysis is performed for n257 and n258 frequency bands (24.5–28.7 GHz). The probability distributions of the design parameters are extracted from the measurement results obtained for a series of manufactured patch antenna arrays for three different frequencies in the C, X, and Ka bands. Patch antennas are chosen as the subject of the scientific analysis in this paper because of the popularity of the patch antennas in the scientific literature concerning antennas, as well as because of a simple form of these antennas that is reflected in the time required for computation of training and testing data for the Artificial Neural Network.

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