Abstract
Purpose of reseach is to develop a method for generating training data to enable the use of artificial neural networks (ANN) method in gas analyzer systems. The problem of increasing the accuracy of separate determination of gas concentrations in multicomponent mixtures under conditions of environmental parameters changes is considered. It is proposed to increase the accuracy of determining target gas concentrations by using the ANN method for joint processing of sensor signals.Methods: Training data for the neural network were generated using numerical experiments and mathematical simulation methods. To assess the accuracy of training, the standard deviation (SD) was used and the relative error was calculated. ANN training and research were conducted in the MATLAB environment (the Neural Networks Toolbox application). When developing mathematical models of gas sensors, the theory of electrical circuits, electronic theory of chemisorption and the adsorption theory of heterogeneous catalysis were applied.Results: A method for generating training data sets using mathematical models of gas sensors is described. The proposed training method has been tested on a specific task, in particular, a decision-making device based on ANN for a four-component gas analyzer has been developed. The efficiency of using neural networks for tuning out from the mutual cross-sensitivity of sensors was evaluated.Conclusion: A method for generating training data using simulation models is proposed, which allows automazing the process of training, research, choosing the architecture and structure of ANN and their testing. The method was tested. Based on the analysis of the obtained errors, conclusions are made about the efficiency of using neural networks to reduce errors caused by cross sensitivity at different concentrations of the main and interfering gases.
Highlights
Measure of carbon dioxide using a gas sensor of a semiconductor type based on tin dioxide (SnO2) / H
Измерительный блок для нейтронного спектрометра реального времени с вычислительным восстановлением энергетических спектров с помощью нейронных сетей // Известия Юго-Западного государственного университета
Summary
Математически исходную задачу можно описать в виде системы, состоящей из n уравнений (n – число датчиков, входящих в состав системы), параметрами которой являются концентрации газов (С), относительная влажность воздуха (RH), температура воздуха (Т), атмосферное давление (P) [7]: S1 f1(C1,C2,..,C j ,..,Ck ,T, P, RH ) Sj f j (C1,C2,..,Cj,..,Ck ,T, P, RH ). Математические модели модерниобучающих и тестирующих данных, зировались и разрабатывались для оспредставляющих собой различные комновных типов датчиков, выпускаемых бинации входных параметров и соответпромышленностью: полупроводникоствующие им сигналы датчиков. Для нахождения неизвестных параметров модели использовалась техническая документация на полупроводниковый датчик монооксида углерода. При нахождении коэффициентов у концентраций газов параметры окружающей среды соответствовали нормальным условиям (Р=101.3 кПа, Т=293,15 К, RH=50%), По результатам нахождения неизвестных параметров математической модели (1) получены следующие значения максимальных относительных погрешностей (σmax) (табл.1)
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