Artificial Neural Network Architecture Design by Increased Evolutionary Learning

2014 ◽  
pp. 16-23
Author(s):  
G. V.R. Sagar ◽  
K. Anitha Sheela
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Architecture Design ◽  
Evolutionary Learning ◽  
Neural Network Architecture ◽  
Artificial Neural Network Architecture ◽  
Artificial Neural

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

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