Joint application of group determination of parameters and of training with noise addition to improve the resilience of the neural network solution of the inverse problem in spectroscopy to noise in data

Abstract

In most cases, inverse problems are ill-posed or ill-conditioned, which is the reason for high sensitivity of their solution to noise in the input data. Despite the fact that neural networks have the ability to work with noisy data, in the case of inverse problems, this is not enough, because the incorrectness of the problem “outweighs” the ability of the neural network. In previous studies, the authors have shown that separate use of methods of group determination of parameters and of noise addition during training of neural networks can improve the resilience of the solution to noise in the input data. This study is devoted to the investigation of joint application of these methods. The study is performed at the example of an inverse problem in laser Raman spectroscopy - determination of concentrations of ions in a solution of inorganic salts by Raman spectrum of the solution.