A comparison between single and combined backpropagation neural networks in the prediction of turnover

Author

Tchaban, T. ; Griffin, J.P. ; Taylor, M.J.

Author_Institution

Dept. of Comput. Sci., Univ. Coll. Chester, UK

Volume

2

fYear

1997

fDate

27-23 May 1997

Firstpage

347

Abstract

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

Keywords

backpropagation; data analysis; financial data processing; forecasting theory; marketing data processing; neural nets; performance evaluation; retail data processing; combined backpropagation neural networks; company performance; data analysis; marketing analysis; nonlinear problems; poorly represented data; retail company; single backpropagation neural networks; turnover prediction; Artificial neural networks; Backpropagation; Business; Companies; Computer science; Intelligent networks; Marketing and sales; Neural networks; Neurons; Transfer functions;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge-Based Intelligent Electronic Systems, 1997. KES '97. Proceedings., 1997 First International Conference on

Conference_Location

Adelaide, SA

Print_ISBN

0-7803-3755-7

Type

conf

DOI

10.1109/KES.1997.619408

Filename

619408