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Short-term prediction of air pollution using TD-CMAC neural network model

Rahmani, A. M ; Sharif University of Technology | 2004

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  1. Type of Document: Article
  2. Publisher: 2004
  3. Abstract:
  4. This paper presents a new model to short-term prediction of air pollution using a new structure is based on the intelligent neural networks. A new structure known as Time Delay Cerebellar Model Arithmetic Computer (TD-CMAC), an extension to the CMAC, it requires fewer memory sizes. The new model is demonstrated and validated with three primary air pollutants known as carbon monoxide (CO), sulfur dioxide (SO2), and nitrogen dioxide (NO 2). The simulation results for the half an hour ahead-prediction of the air pollutant data set show that the suggested new model is suitable for our purpose
  5. Keywords:
  6. Air Pollution ; Neural Networks ; Short-Term Prediction ; Time Delay CMAC (TD-CMAC) ; Time Series
  7. Source: Soft Computing with Industrial Applications - International Symposium on Soft Computing for Industry, ISSCI - Sixth Biannual World Automation Congress, WAC 2004, Sevilla, 28 June 2004 through 1 July 2004 ; 2004 , Pages 357-362 ; 1889335231 (ISBN)
  8. URL: https://ieeexplore.ieee.org/document/1439392