A hybrid learning algorithm for Interval Type-2 Fuzzy Neural Networks in time series prediction for the case of air pollution

Author

Castro, J.R. ; Castillo, O. ; Melin, P. ; Rodriguez-Diaz, A.

Author_Institution

Baja California Autonomous, Univ. Autonoma de Baja California, Tijuana

fYear

2008

Firstpage

1

Lastpage

6

Abstract

Air pollution is a growing problem arising from domestic heating, high density of vehicle traffic, electricity production, and expanding commercial and industrial activities, all increasing in parallel with urban population. Monitoring and forecasting of air quality index in urban areas is important due to health impact. Hybrid intelligent techniques are successfully used in modeling of highly complex and non-linear phenomena. In this paper, interval type-2 fuzzy neural network (IT2FNN) hybrid method has been proposed to predict the impact of meteorological pollutants on ozone (O₃) over an urban area. The IT2FNN model forecasts trends in O₃ with high performance.

Keywords

air pollution control; fuzzy neural nets; neurocontrollers; time series; air pollution; domestic heating; electricity production; hybrid intelligent techniques; hybrid learning algorithm; interval type-2 fuzzy neural network; meteorological pollutants on ozone; time series prediction; vehicle traffic; Air pollution; Electric vehicles; Fuzzy neural networks; Production; Resistance heating; Telecommunication traffic; Thermal pollution; Traffic control; Urban areas; Weather forecasting;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information Processing Society, 2008. NAFIPS 2008. Annual Meeting of the North American

Conference_Location

New York City, NY

Print_ISBN

978-1-4244-2351-4

Electronic_ISBN

978-1-4244-2352-1

Type

conf

DOI

10.1109/NAFIPS.2008.4531338

Filename

4531338