Abstract
An algorithm for finding a sufficient amount of a representative set of sample data for training artificial neural networks using the numerical parameters of the original sample set is proposed. The algorithm is carried out on the example of analyzing a vector signal generated by a gas analytical multisensor micro-arrays based on a thin SnO2 film when calibrated to the effect of CO, isopropanol, and ethanol in a mixture with air. As a result of the operation of the algorithm, the minimum required volume of the training sample of artificial neural networks was found, which allows achieving a high (more than 99 %) recognition quality. The results show the efficiency of the proposed algorithm.
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