Approximation of Inverse Models for Temperature-Concentration Dependences of the Transmission Function of a Single-Component Homogeneous Gas Medium by Artificial Neural Networks

Abstract

The problem of application of artificial neural networks for approximation of inverse models of temperature-concentration dependences of the transmission function of a single-component homogeneous gas medium is considered on the example of carbon monoxide. The gas transmission function is calculated using the line-byline method for five spectral centers at partial pressures 0.1–1 atm and temperatures 300–2500 K. The inverse models are approximated using a multilayered perceptron with three hidden layers. The artificial neural network is learned using the Levenberg–Marquardt algorithm with Bayesian regularization. The errors of the obtained inverse models are analyzed depending on the number of the employed spectral centers and the leaning sample size. A tendency toward a decrease in error values with increase of these parameters is demonstrated. Maximal steps of the uniform concentration-temperature grid required for correct approximation of the inverse models by the artificial neural networks are determined. The inverse model of the temperature-concentration dependence of the carbon monoxide transmission function, providing a solution of the inverse optical problem on the determination of its partial pressure and temperature, is obtained with relative errors less than 3% in the examined ranges of their variations.