A new winners-take-all architecture in artificial neural networks

MAXNET is a common competitive architecture to select the maximum or minimum from a set of data. However, there are two major problems with the MAXNET. The first problem is its slow convergence rate if all the data have nearly the same value. The second one is that it fails when either nonunique extreme values exist or each initial value is smaller than or equal to the sum of initial inhibitions from other nodes. In this paper, a novel neural network model called SELECTRON is proposed to select the maxima or minima from a set of data. This model is able to select all the maxima or minima via competition among the processing units even when MAXNET fails. We then prove that SELECTRON converges to the correct state in every situation. In addition, the convergence rates of SELECTRON for three special data distributions are derived. Finally, simulation results indicate that SELECTRON converges much faster than MAXNET