Decision Support System Using Artificial Neural Network to Predict Rice Production in Phimai District, Thailand

Saisunee Jabjone, Sura Wannasang
2014 International Journal of Computer and Electrical Engineering  
In Thailand, nowadays, the planted area, climate and rainfall are changed rapidly effect to unstable of Thai rice production. The decision-making processes often require reliable rice response models. Local governor and farmer need simple and accurate estimation techniques to predict rice yield in the planning process. This study aims to develop the decision support system using Artificial Neural Networks (ANN) by adjust the value of parameters and study about 9 Algorithms training. In
more » ... g rice productions which its study found that each values that was adjusted making high predicting like appropriate number of hidden nodes to model equals to 9, learning rate effects the speed of appropriate learning to the model equals to 0.5, and appropriate momentum to model was 0.5. CGB Algorithm has coefficient decision higher than using regression variable technique by Stepwise multiple method curve of ANN and stepwise multiple regression method was 4,293.70 and 40,160.00, respectively. Index Termsneural networks, stepwise multiple regression method.
doi:10.7763/ijcee.2014.v6.814 fatcat:2byhlak37vasbkf7btcikapgs4