Technology of information processing of normalized systems

To simplify the computational process of models of any complexity, there was a need to develop a technology for processing information in the form of a software algorithm that allows you to process, analyze and predict the future process. To convert input data into source information, it is necessary to have a local information conversion algorithm based on the inductive method of synthesizing these algorithms. In the first stage of any probabilistic synthesis algorithm, the algorithm for information transformation is described in the article as the representation of the input data in the form of a predefined system of conditional equations. In the following, the system is presented in a form that allows you to determine the coefficients of the system. And then the concept of "normalization" of the system is introduced, using the method of least squares. The system's research takes place from the point of view of its arguments, which makes it possible to obtain the necessary trained model. All received modifications of the trained models were checked for an error, which is the mean square prediction error for checking the predicted sequence

conditional equations; experimental points; system; normalization; model; error; forecasting