Using Neural Networks to Identify Parameters of Autoregressive Model with Multiple Roots of Characteristic Equations

Number of neural networks was trained to determine the parameters of autoregressive models with multiple roots of characteristic equations. The text deals with special cases of such a model including the first and second order models. Efficiency studies were conducted for neural networks with back propagation of error, networks of cascade type and networks of generalized regression. The identification results by neural networks are compared with an identification algorithm based on correlation functions. The proposed algorithms are implemented both for one-dimensional random sequences based on such models, and for two-dimensional random fields. In addition, the parameters were identified for fitting models to real images.