DocumentCode :
3208125
Title :
Forecasting global Internet growth using fuzzy regression, genetic algorithm and neural network
Author :
Bagchi, Kallol ; Mukhopadhyay, Somnath
Author_Institution :
Dept. of IDS, Texas Univ., El Paso, TX, USA
fYear :
2004
fDate :
8-10 Nov. 2004
Firstpage :
543
Lastpage :
548
Abstract :
This paper introduces several soft models such as the genetic algorithm, neural network and fuzzy regression to study and predict the Internet growth in several OECD nations. First a linear version of an augmented diffusion model is designed. The augmented diffusion model is designed by including the impact of an economic indicator, GDP per capita in to the model. In the next stage the soft models are built, using the augmented diffusion model as the base model. Performance and forecasting measures from these soft models show that these soft models provide improvements over the augmented diffusion model. We also discuss how the information from the models can be reused and integrated.
Keywords :
Internet; economic indicators; forecasting theory; fuzzy logic; genetic algorithms; neural nets; regression analysis; uncertainty handling; augmented diffusion model; economic indicator; fuzzy regression; genetic algorithm; global Internet growth forecasting; neural network; soft models; Decision making; Economic forecasting; Fuzzy neural networks; Genetic algorithms; IP networks; Information technology; Internet; Neural networks; Predictive models; Uncertainty;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Reuse and Integration, 2004. IRI 2004. Proceedings of the 2004 IEEE International Conference on
Print_ISBN :
0-7803-8819-4
Type :
conf
DOI :
10.1109/IRI.2004.1431517
Filename :
1431517
Link To Document :
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