Design of the models of neural networks and the Takagi-Sugeno fuzzy inference system for prediction of the gross domestic product development

Authors:
Vladimír Olej
Affiliations:
Institute of System Engineering and Informatics, Faculty of Economics and Administration, University of Pardubice, Pardubice, Czech Republic
Venue:
NN'05 Proceedings of the 6th WSEAS international conference on Neural networks
Year:
2005

Citing 2
Cited 1

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Prediction of the index fund by Takagi-Sugeno fuzzy inference systems and feed-forward neural network

AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases

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Abstract

The paper presents the possibility of the design of frontal neural networks and feed-forward neural networks (without pre-processing of inputs time series) with learning algorithms on the basis genetic and eugenic algorithms and Takagi-Sugeno fuzzy inference system (with pre-processing of inputs time series) in predicting of gross domestic product development by designing a prediction models whose accuracy is superior to the models used in praxis [1,2].