IEEE ... International Conference on Neural Networks, Volume 1; Volumes 3-7SOS Printing, 1994 - Artificial intelligence |
Contents
NEURAL NETWORK HARDWARE PERFORMANCE CRITERIA 1885 | xxxi |
ADAPTIVE RESONANCE THEORY NEURAL NETWORKSINVITED SESSION | xlviii |
RANDOM PARAMETER VARIATION IN ANALOG VLSI NEURAL NETWORKS FOR LINEAR | l |
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activation function adaptation applications approach approximation ARTIFICIAL NEURAL NETWORKS backpropagation algorithm Bernoulli error measure binary boundary classification complexity Computer connection weights convergence data set defined denotes distribution dynamic encoding Engineering epochs equation error function estimate example feedforward neural networks Figure given GMDP gradient descent hidden layer hidden neurons hidden nodes hidden units hyperplane IEEE implementation input pattern input vector iteration learning algorithm learning rate learning speed linear mapping method minimization multi-valued functions multilayer multilayer perceptron neural net neuron nonlinear number of hidden optimal output layer output neuron output units parallel parameters perceptron performance problem processor propagation proposed random recurrent RECURRENT NEURAL NETWORKS Rumelhart shown sigmoid sigmoid function signal simulation space squared error structure subnetworks subset success success supervised learning target Technology Theorem tion training data training patterns training set University update values