Using ENN-1 to Inspect the Air Pollution of Automobile Exhaust by Remote Sensing Data

This research uses the extension neural network type-1 (ENN-1) method for air pollution inspected by remote sensing data of automobile´s exhaust. The outdated automobiles emit exhaust as part of the moving air pollutants. To lessen the air pollution effectively and improve the efficiency of remote sensing tools, this paper develop a automatic inspected method based on the ENN-1 and using the data of automobile exhausted telemeter, the ENN-1 can embed the salient features of parallel computation and learning capability. The experimental results show that the proposed method has less learning time, high classificatory accuracy rate, and fault-tolerant than the other methods