DocumentCode :
1951062
Title :
Comparison of Hybrid Intelligent Systems, Neural Networks and Interval Type-2 Fuzzy Logic for Time Series Prediction
Author :
Castillo, Oscar ; Melin, Patricia
Author_Institution :
Tijuana Inst. of Technol., Tijuana
fYear :
2007
fDate :
12-17 Aug. 2007
Firstpage :
3086
Lastpage :
3091
Abstract :
Uncertainty is an inherent part of intelligent systems used in real-world applications. The use of new methods for handling incomplete information is of fundamental importance. Type-1 fuzzy sets used in conventional fuzzy systems cannot fully handle the uncertainties present in intelligent systems. Type-2 fuzzy sets can handle such uncertainties in a better way because they provide us with a more complete model of real-world uncertainty. Experimental results are also presented for forecasting chaotic time series in which interval type-2 fuzzy logic outperforms some hybrid intelligent approaches. Neural networks provide a comparable result with type-2 fuzzy systems.
Keywords :
fuzzy logic; fuzzy set theory; fuzzy systems; neural nets; time series; hybrid intelligent system; interval type-2 fuzzy logic; neural network; time series prediction; type-1 fuzzy set; Chaos; Economic forecasting; Fuzzy logic; Fuzzy sets; Fuzzy systems; Hybrid intelligent systems; Intelligent networks; Neural networks; Signal processing algorithms; Uncertainty;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location :
Orlando, FL
ISSN :
1098-7576
Print_ISBN :
978-1-4244-1379-9
Electronic_ISBN :
1098-7576
Type :
conf
DOI :
10.1109/IJCNN.2007.4371453
Filename :
4371453
Link To Document :
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