The book discusses the theoretical foundations of modeling artificial neural networks of various architectures. Algorithms of training of single-layer and multi-layer direct distribution networks, self-organizing and recurrent networks are given. Modeling of multiagent systems based on evolving neural networks is considered. Original methods for visualizing the internal state of a trained neural network and solving the problems of classification, categorization, forecasting, and restoration of noisy information are given.