Title :
Short-term prediction of air pollution using td-cmac nfural network model
Author :
Rahmani, A.M. ; Teshnehlab, M. ; Abbaspour, Maghsood ; Setayeshi, S.
fDate :
June 28 2004-July 1 2004
Abstract :
This paper presents a new model to shon-term prediction of air pollution using new structure is based on the intelligent neural networks. A new structure known as Time Delay Cerebellar Model Arithmetic Conipucer (TO-CMAC), an cxtension to rhe CMAC, i t requires fewer niemory sizes. The ncw model is denionmated and validated with three priiiiary air pollulants known as carhon monoxide (CO), ,sulfur dioxide (SO2), and nitrogen dioxide (NO2). The siiiitilation results for the half an hour ahead-prediction of the air pollutant data set show that the suggested new model is witable for our purpose.
Keywords :
Air pollution; Arithmetic; Artificial neural networks; Biological neural networks; Delay effects; Fuzzy systems; Input variables; Neural networks; Physics; Predictive models;
Conference_Titel :
Automation Congress, 2004. Proceedings. World
Conference_Location :
Seville
Print_ISBN :
1-889335-21-5