A Fast Winner-Take-All Neural Networks With the Dynamic Ratio

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 win-ner-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 inhibi-tion. 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 simu-lation results