In this paper, we propose a fast winner-take-all (WTA) neural network. The fast winner-take-all neural network with the dynamic ratio in mutual-inhibition is developed from the general mean-based neural network (GEMNET), which adopts the mean of the active neurons as the threshold of mutual inhibition. Furthermore, the other winner-take-all neural network enhances the convergence speed to become a decimal system. The proposed WTA neural networks statistically achieve the large ratio of mutual inhibition. The new WTA Neural Networks converge faster than the existing WTA neural networks for a large number of competitors based on both theoretical analyses and simulation results.

Keywords: winner-take-all, neural network, convergence speed, decimal system, mutual inhibition

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Received July 29, 1999; revised March 30 & September 6, 2000; accepted October 26, 2000.
Communicated by Chuen-Tsai Sun.