A fast maxnet
Yi Chuan Chang, Chung J. Kuo · Journal of the Chinese Institute of Engineers · 2003
Winner‐Take‐All (WTA) networks frequently appear in neural networks. They are primarily used for decision making and selection. Maxnet is a WTA network; it can select the maximum from a data set. However, there are two essential drawbacks to the Maxnet. The first drawback is its slow convergent speed if the input data have almost the same values or the amount of input data is very large. The second one is that it fails when nonunique extreme values exist. In this paper, a new WTA network model called Fast Maxnet is proposed to select the maximum from a data set and be able to converge to the correct state in any situation. Simulation results indicate that the fast Maxnet converges much faster than the Maxnet.