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 The outdated automobiles emit exhaust as part of the moving air pollutants.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