ALGORITMA PROPAGASI BALIK DALAM PENCARIAN POLA TRAINING TERBAIK UNTUK MENENTUKAN PREDIKSI PRODUKSI USAHA SONGKET SILUNGKANG DENGAN MENGGUNAKAN MATLAB

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Rima Liana Gema ◽  
Devia Kartika
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Backpropagation Algorithm ◽  
Propagation Algorithm ◽  
Testing Stage ◽  
West Sumatra

One method used in Artificial Neural Networks is a backpropagation algorithm that is widely used in predicting and pattern recognition. Songket is one of the works of skilled hands of the original Silungkang craftsmen, Sawahlunto City, West Sumatra who have varied and unique patterns and motifs. This study uses a back propagation algorithm to find the best training pattern to facilitate the determination of the production prediction of Silungkang songket business using the Matlab application. The best training patterns obtained are expected to be used in data processing at the testing stage in order to obtain predictions for the production of songket business for the future. Keywords: production, songket, back propagation.

Artificial neural networks back propagation algorithm for cutting force components predictions

2013 ◽  
Vol 14 (6) ◽  
pp. 431-439 ◽  
Author(s):  
Issam Hanafi ◽  
Francisco Mata Cabrera ◽  
Abdellatif Khamlichi ◽  
Ignacio Garrido ◽  
José Tejero Manzanares
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cutting Force ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Cutting Force Components ◽  
Artificial Neural ◽  
Force Components

scholarly journals Prediction of ground subsidence due to underground mining through time using multilayer feed-forward artificial neural networks and back-propagation algorithm – case study at Mong Duong underground coal mine (Vietnam)

2021 ◽  
Vol 6 (4) ◽  
pp. 241-251
Author(s):  
Q. L. Nguyen ◽  
Q. M. Nguyen ◽  
D. T. Tran ◽  
X. N. Bui
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Coal Mine ◽  
Underground Mining ◽  
Back Propagation ◽  
Ground Subsidence ◽  
Back Propagation Algorithm ◽  
Underground Coal Mine ◽  
Network Training ◽  
Propagation Algorithm

The paper is devoted to studying the possibility of using artificial neural networks (ANN) to estimate ground subsidence caused by underground mining. The experiments showed that the most suitable network structure is a network with three layers of perceptrons and four neurons in the hidden layer with the back propagation algorithm (BP) as a training algorithm. The subsidence observation data in the Mong Duong underground coal mine and other parameters, including: (1) the distance from the centre of the stope to the ground monitoring points; (2) the volume of mined-out space; (3) the positions of the ground points in the direction of the main cross-section of the trough; and (4) the time (presented by cycle number), were used as the input data for the ANN. The findings showed that the selected model was suitable for predicting subsidence along the main profile within the subsidence trough. The prediction accuracy depended on the number of cycles used for the network training as well as the time interval between the predicted cycle and the last cycle in the training dataset. When the number of monitoring cycles used for the network training was greater than eight, the largest values of RMS and MAE were less than 10 % compared to the actual maximum subsidence value for each cycle. If the network training was less than eight cycles, the results of prediction did not meet the accuracy requirements.

scholarly journals Reflectivity Parameter Extraction from RADAR Images Using Back Propagation Algorithm

2018 ◽  
Vol 8 (5) ◽  
pp. 2795
Author(s):  
P. Anil Kumar ◽  
B. Anuradha
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Back Propagation ◽  
Likelihood Estimation ◽  
Convective Cloud ◽  
Parameter Extraction ◽  
Distance Measures ◽  
Back Propagation Algorithm ◽  
Backpropagation Algorithm ◽  
Radar Images

<p class="Abstract">Pattern recognition has been acknowledged as one of the promising research areas and it has drawn the awareness among many researchers since its existence at the beginning of the nineties. Multilayer Neural networks are used in pattern Recognition and classification based on the features derived from the input patterns. The Reflectivity information extracted from the Doppler Weather Radar (DWR) image helps in identifying the convective cloud type which has a strong relation to the precipitation rate. The reflectivity information is rooted in the DWR image with the help of colors and color bar is provided to distinguish among different reflectivity information. Artificial Neural network predicts the color based on the maximum likelihood estimation problem. This paper presents a best possible backpropagation algorithm for color identification in DWR images by comparing various backpropagation algorithms such as LevenbergMarquardt, Conjugate gradient, and Resilient back propagation etc.,. Pattern recognition using Neural networks presents better results compared to standard distance measures. It is observed that Levenberg-Marquardt backpropagation algorithm yields a regression value of 99% approximately and accuracy of 98%</p>

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