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Ozone concentration forecasting with neuro-fuzzy approaches

Abdollahzade, M ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/ICSCCW.2009.5379469
  3. Abstract:
  4. Forecasting is a challenging problem in highly nonlinear dynamic systems. The main goal in development of forecasting models in complex systems is to produce a model that can accurately behave similar to the main system. In problems such as air pollution forecasting, the presence of uncertainties and nonlinearities affects the model's precision. In this paper, ozone concentration, which is well-known as an index for air pollution is forecasted using neuro-fuzzy models. Causal variables are integrated in the models in order to enhance the model's performance. The results are compared to a fuzzy logic approach to demonstrate reliability and accuracy of the proposed model using real observed data
  5. Keywords:
  6. Air pullution ; Complex systems ; Forecasting models ; Fuzzy logic approach ; Highly nonlinear ; Neuro-Fuzzy ; Neuro-fuzzy approach ; Neuro-Fuzzy model ; Non-Linearity ; Observed data ; Ozone concentration ; Air quality ; Concentration (process) ; Dynamical systems ; Fuzzy logic ; Ozone ; Soft computing ; Systems analysis ; Forecasting
  7. Source: ICSCCW 2009 - 5th International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, 2 September 2009 through 4 September 2009, Famagusta ; 2009 ; 9781424434282 (ISBN)