Functional Corroboration of a Digital Multilayer Neural NetworkMichigan State University. Department of Electrical Engineering, 1995 - 258 Seiten |
Inhalt
BACKGROUND | 9 |
DIGITAL MULTILAYER NEURAL NETWORKS | 41 |
DMNN ANALYSIS AND APPLICATIONS | 89 |
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5-Digit Classifier 8x3 Encoder Action potentials applied artificial neural networks aw jk axon backpropagation algorithm Block Diagram Boltzmann Machine classification problems clock connection weight Delta Rule developed by Kim Digital Comparator digital multilayer neural DMNN architecture DMNN coprocessor Electrical Synapse error surface excitatory Ənet feedforward feedforward neural network Figure gradient descent Hardware Description Language hidden layer neuron Hopfield Ideal Digits IEEE inhibitory input layer input pattern interconnected Kim's learning rate learning rule LFSR logic gates mathematical modified DMNN Momentum Factor momentum term multilayer neural network neural network DMNN node noisy data noisy patterns nonlinear Number of Iterations number of neurons output layer perceptron performance presented processing element pulse occurrence pulse sequences sigmoidal function software simulation statistically independent Step stochastic computing techniques stochastic function Synaptic Element synaptic weights Test Patterns updated V₁ values VHDL Vi(n weight change weights connecting XOR problem XOR Solver дек