An electronic nose natural neural learning model in real work environment

Abstract

The natural identification of an odor, smelled once, highlights a new idea on the artificial intelligence for electronic noses to mimic as close as possible the biological olfactory system. For the first time, a natural on-line training with only one sample, to extract both eigen-weights and eigen-bias, for each odor is built herein to elaborate a natural identifier neural model in a real work environment. The proposed model efficiently reduces the maximum extent of traditional neural models complexities, namely generic work-laboratory, dimensional data learning (typically from 60 to 80%), model adaptability complication, time-consuming, heavy experiment materials and chemical products. The outcomes show that the invented model identifies correctly 100% the three hazardous contaminants of the test data (all data minus only three samples for learning) processed from gas sensors weakened in the harshest conditions. Therewith, this natural identifier model methodology is elaborated easily enough to be efficient in different environments conditions to accurately control the air quality safety in any local area.