The hybrid neural model to strengthen the e-nose restricted in real complex conditions

The assessment sensors’ signals, noised in real complex condition, is a serious problem, which restrains the performance of the neural model application laboratory in its real application. Indeed, the Self-Organizing Maps (SOM) and neural multilayer perceptron (MLP) are fused to reinforce the olfactory devices power for optimum pollutants identification. As soon as the SOM is developed, with the whole key parameters, carefully selected (as Epanichnikov kernel function), to automatic elimination of uninformative signals, the few useful signals are MLP trained to optimally determine the model to improve the results performance. Fast precise identification, short execution duration, small local data, having strong signals selected, are all required to attenuate the sensors several criticalities emerged from the lowest content (1ppm) of interfering mixed gases at relative humidity presence. Moreover, the practical application of this hybrid tool on 1ppm concentration has another advantage to ensure the air quality, either in critical condition control or in safety.