Development of neural network forecasting models of dynamic objects from observed data

The paper investigates the issues of constructing predictive models of the state of dynamic models based on neural network structures, which are a powerful mathematical apparatus for approximating various types of functions and assessing the dynamics of changes in the states of the objects under consideration. A method is proposed for constructing predictive neural network models for the observed data of real-life objects using a hybrid application of the theory of nonlinear dynamics and nononical networks. The neural network predictive model built on the basis of the reconstructed phase trajectories of the process (attractor) will allow choosing the most informative characteristics, minimizing the architecture of the neural network and the dimension of the vector from the measured controlled signals. Based on the developed algorithms for preprocessing local areas of the reconstructed attractor and the selected machine learning method, a scheme for constructing a neural network predictive model of the dynamic behavior of a structurally complex system is proposed.

Перевод пока недоступен