Neural Networks Associated with the “Black Box” Models of Non-Linear Dynamic Systems

The approximation of non-linear dynamic system operators by way of describing the input-output relationship with the help of mathematical models is considered. A neural network is one of famous mathematical models. The types of neural networks are represented as the universal approximators of nonlinear operators. The classification of recurrent neural networks according to the feedback location is described. The recurrent Hammerstein network is used as a mathematical model of a non-linear compensator for digital communication channels. The source of non-linear signal distortion in communication channels is a power amplifier. It is found that the model of a non-linear compensator in the form of the recurrent Hammerstein network exceeds the two-layer perceptron network in the accuracy of signal processing and the Volterra polynomial in the simplicity of hardware implementation.