Gas Analysis System Composed of a Solid-state Sensor Array and Hybrid Neural Network Structure

The paper presents the application of the hybrid neural network to the solution of the calibration problem of the solid state sensor array used for the gas analysis. The applied neural network is composed of two parts: the selforganizing Kohonen layer and multilayer perceptron (MLP). The role of the Kohonen layer is to perform the feature extraction of the data and MLP network fulfills role of the estimator of the concentration of the gas components. The obtained results have shown that the array of partially selective sensors, cooperating with hybrid neural network, can be used to determine the individual analyte concentrations in a mixtures of gases with good accuracy. The hybrid network is a reasonably small net and thanks to this it learns faster and reaches good generalization ability at reasonably small size of training data set. The system has the two interesting features: lower calibration cost and good accuracy.