Abstract
One of the most recent and perspective gas detectors is a smart waveguide acoustic detector, in which chromatogram represents the mass concentration of the gas to be detected. On the other hand, with respect to design criterions and limits (cost and size), an alternative numerical-based detector has been designed, using a multi-layer perceptron neural network to estimate the frequency and mass concentration of the unknown gas (sample). Experimental data were used for designing the database of the neural network-based detector. The proposed NN-based detector was compared with the real one to validate the proposal. Finally, numerical results were obtained to evaluate its performance.