Carbon monoxide prediction using novel intelligent network

ABSTRACT

This paper introduces a new structure in neural networks called TD-CMAC, an extension to the conventional Cerebellar Model Arithmetic Computer [CMAC], having reasonable ability in time series prediction. TD-CMAC, the conventional CMAC and a classical neural network model called Multi-Layer Perceptron [MLP] are simulated and evaluated for 1-hour-ahead prediction and 24-hour-ahead prediction of carbon monoxide as one of primary air pollutants. Carbon monoxide data used in this evaluation were recorded and averaged at Villa station in Tehran, Iran from October 3 rd. 2001 to March 14 th. 2002 at one-hour intervals. The results show that the errors made by TD-CMAC is fewer than those made by other models