Science of Artificial Neural Networks, Volume 1, Parts 1-2SPIE, 1992 - Neural networks (Computer science) |
Contents
Computational learning theory Plenary Paper 171003 | 3 |
Architectures | 19 |
R Michaels Univ of TennesseeKnoxville | 32 |
Copyright | |
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Common terms and phrases
AANET activation function analysis applied approximation architecture artificial neural networks automata backpropagation behavior binary classification Computer configuration convergence correlation defined distal dynamics elements equations error example faults feature feedforward networks Figure global grammar hidden layer hidden nodes hidden units IEEE input matrix input vectors iterations learning algorithm learning potential function linear Lyapunov exponents mapping mathematical method minimization multilayer multilayer perceptron neural net neurons noise nonlinear null space number of hidden objective function optimal optimisation oscillators output layer output node parameters pattern recognition performance perturbation phase transition pixels principal component probability distribution problem propagation receptive field receptors representation represents rule samples self-organizing sigmoid sigmoid functions signals simulated simulated annealing single-layer solution space statistical structure supervised learning target technique theory threshold tooth training patterns training set transformations two-layer perceptron variables vector visual weights XX XX