A comparison between single and combined backpropagation neural networks in the prediction of turnover
作者
T. Tchaban,J. P. Griffin,Michael Taylor
标识
DOI:10.1109/kes.1997.619408
摘要
Artificial neural networks are now being extensively used in the area of marketing analysis as they are well suited to this type of non-linear problem. A retail company planned to improve its performance by using neural networks to predict turnover and data used in the experiment was provided by the company. The study compares the performance of a combination of neural networks to that of a single neural network. The results show that backpropagation neural networks are effective tools which can give good results in solving a non-linear prediction problem, even when data is poorly represented.