EVOLUTIONAL DEVELOPMENT OF A MULTILEVEL NEURAL-NETWORK

The paper describes changes in the structure (anatomy) of a multilevel neural network which is trained by back-propagation algorithm. A structural change is both a random process and an environment-dependent process. The mechanisms of cell propagation and degeneration of inactive synapses and inactive cells are considered. The change in the neural network structure occurs during the training and running of the system, which enables dynamic adaptation of the network capacity to the complex problem of the network interaction with the environment. Structural changes in a multilevel neural network are tested on several tasks of the network training.