Generalized hamming networks and applications
Neural Networks
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The convergence properties of Hamming memory networks are studied. It is shown how to construct the network so that it probably converges to an appropriate result, and a tight bound is given on the convergence time. The bound on the convergence time is largest when several stored vectors are at the minimum distance from the input vector. For random binary vectors, the probability for such situations to occur is not small. With a specific choice of parameter values, the worst-case convergence time is on the order of p ln (pn), where p is the memory capacity and n is the vector length. By allowing the connection weights to change during the computation, the convergence time can be decreased considerably