Short-term prediction of air pollution using td-cmac nfural network model

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.