Introduction. Different approaches to modeling and using of neural networks
Features of construction and working of brain
Binary neural network classifiers
Logical neural networks
Hemming neural network
Multi-Layer Neural networks.
Forward neural networks
Back propagation algorithm
Different kinds of learning of MLP
Using of MLP for forecasting of time series.
Counterpropagation neural networks
Recurrent multi-layer neural networks
Using of recurrent multi-layer neural networks for recognition of grammatically correct sentences.
Using of recurrent multi-layer neural networks for control of robots. LSTM-neural networks.
Radial basis functions
BAM/Hopfield associative memory
Adaptive resonance theory
Architecture ARTMAP, FuzzyART
Spike neural networks.
Using of Genetic Algorithms for learning and evolution of neural networks
New trends in development of neural networks
Condition for passing of midterm exam – selection of task for solving by NN,
Selection of kind of NN, learning method, training set, tool for implementation.
Condition of passing of final exam – implementation of NN and presentation of experiments with it.