Neural computing: theory and practice
Neural computing: theory and practice
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
A new adaptive merging and growing algorithm for designing artificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Future Generation Computer Systems
Parameter genetic learning of perceptron networks
ICS'06 Proceedings of the 10th WSEAS international conference on Systems
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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.