Artificial Neural Network Based Model for Forecasting Sugar Cane Production | Science Publications

Journal of Computer Science

O. O. Obe¹  D. K. Shangodoyin¹ 

关键词: Artificial neural networks;    processing elements;    performance measures;    test;    set data;    training set;   

DOI  :  10.3844/jcssp.2010.439.445

学科分类：计算机科学（综合）

来源: Science Publications

【 摘 要 】

Problem statement: The global need for alternative energy source has necessitated the exploration of vast organic agricultural products with a view of processing them for the production of ethanol in commercial quantity. To ascertain a sustainable production of ethanol from processed sugar cane, a predictive model based on non-linearity nature of its production is imperative. This is due to unavailability of sufficient reliable data and the wide yield fluctuation that was not well dispersed over time.Approach: This study employed heuristic technique to develop an Artificial Neural Network (ANN) model to forecast sugar cane production in Nigeria. The input data set used includes the socio-economic and agro-climatic factors affecting sugar cane production while the output is the actual sugar cane output for the period covering 1920-2005. Various hidden layers and processing elements were tested giving rise to different Artificial Neural Network (ANN) models. The performance of the ANN models were measured using the Mean Squared Error (MSE), Normalized Mean Squared Error (NMSE), correlation coefficient (r), Akaike's Information Criterion (AIC) and Minimum Description Length (MDL). The contributions of the inputs to the outputs were determined to know how variation of the input variables affected the output.Results: The 85.70% accuracy result of the best ANN model of 2-hidden layer network of 4 Processing Elements (PEs) indicated the efficacy of Artificial Neural Network in accurate prediction. Conclusion: The developed ANN based model fits well real data and can be used for predicting purpose with a high accuracy.

【 授权许可】

Unknown