Method of training examples in solving inverse ill-posed problems of spectroscopy

Further development of the method of computational experiments for solving ill-posed problems is given. The effective (unoverstated) estimate for solution error of the first-kind equation is obtained using the truncating singular numbers spectrum of an operator. It is proposed to estimate the magnitude of the truncation by results of solving model (training, learning) examples close to the initial example (problem). This method takes into account an additional information about the solution and gives a new principle for choosing the regularization parameter and error estimate for equation solution by the Tikhonov regularization method. The method is illustrated by a numerical example from the inverse problem of spectroscopy.

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