Automation of recognition of chemicals using electronic sensor technology based on neural network data processing

The composition of the initial substance was determined using an electronic sensor “electronic nose”, consisting of 8 sensors, to which air was supplied with a syringe with alcohol vapor containing various kinds of impurities. The signal from the sensors was recorded with a sampling frequency of 1 s for 120 s. The output of the device was presented in two different interpretations - in the form of curves obtained from each sensor, or the areas under the curves. The purpose of the work is to build a recognition system for 11 impurities and water in the starting material. The composition of the initial substance was determined using an “electronic nose”, which allows one to obtain 120 values from each of 8 sensors in the form of curves or the values of the areas under the curves. A large number of classes (12), the dynamic presentation of the source data information make it advisable to build a pattern recognition system based on a neural network - a multilayer perceptron trained on the basis of the error back propagation algorithm. When training the network, existing samples are used, indicating which class they belong to. The properties of each analyte are represented as a vector of 120 values of 8 attributes defining one of 12 classes. To reduce the dimensionality of the input data of the neural network, the authors proposed the use of convolution of the available information without significant loss of information capacity of signs by constructing 8 polynomial regressions of the 19th order that describe the curves from each of the 8 sensors of the “electronic nose”. The input matrix obtained as a result of convolution consisted of 20 polynomial regression coefficients of each of 8 curves for 12 classes under consideration. A two-layer neural network with 43 neurons and a sigmoidal activation function in the hidden layer and 12 neurons and a linear activation function in the output layer was constructed. As a result of network training, 2 classification errors were obtained, which allows us to use the approach proposed by the authors to build a recognition system based on preliminary convolution of data dynamically obtained from the “electronic nose