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Three circuits are described as an initial step toward implementing an analog VLSI-based backpropagation neural network. One of these circuits is the connectivity matrix for a fully connected five-input perceptron. The second is a summer circuit that immediately computes total backpropagated error. The third is a triggerable processor that optimizes a given synaptic weight with respect to backpropagated...
The single-layer feedforward neural network (FFNN) in conjunction with the backpropagation training algorithm (BPTA) is used for electrocardiogram (ECG) classification. It has been observed that, for such a problem, the values of the input weights are closely related to the input training set. An implication of this observation is that, rather than choosing initially random weights for the BPTA, one...
An analog VLSI circuit approach to a radial basis function (RBF) neural network is explored. For each of a number of reference pattern templates, the circuit calculates the Euclidean distance between that template and an unknown point, and maps each distance to a point on the Gaussian surface of that template. Then, these points may either be added in order to form the basis for an RBF approximator...
An attempt was made to develop a small, expandable neural network that would oscillate when presented with an appropriate stimulus. The focus of the research is the modification and expansion of a single neuron simulation into a simulation of an interconnected multineuron oscillatory network. A single neuron model based on the work of Hodgkin-Huxley is used to develop a simulation of a generalized,...
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