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Design of two architectures of asynchronous binary neural networks using linear programming
Conference proceeding

Design of two architectures of asynchronous binary neural networks using linear programming

M Kam, J.C Chow and R Fischl
29th IEEE Conference on Decision and Control, v 5, pp 2766-2767 vol.5
1990
DOI: https://doi.org/10.1109/CDC.1990.203280

Abstract

Algorithm design and analysis Convergence Error correction Hamming distance Linear programming Neural networks Neurofeedback Neurons Stochastic processes Very large scale integration
A novel design technique for asynchronous binary neural networks is proposed. This design uses linear programming to design two architectures: (i) a fully connected network that reads a N-digit cue and classifies it into a category represented by a N-digit pattern: and (ii) a two-layer network (with lateral connections) that has M neurons in the first layer and L neurons in the second layer; the network reads an M-digit cue to the first layer and associates it with a second-layer L-digit pattern. In both cases, the objective function is a weighted sum of the number of errors that can be corrected by the network. A cue with this number of errors (or fewer) is guaranteed to converge to the correct pattern. An economical VLSI realization of the designed networks can be easily accomplished.< >

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