DocumentCode :
315568
Title :
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
Link To Document :
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