Layer Winner-Take-All neural networks based on existing competitive structures

In this paper, we propose generalized layer winner-take-all (WTA) neural networks based on the suggested full WTA networks, which can be extended from any existing WTA structure with a simple weighted-and-sum neuron. With modular regularity and local connection, the layer WTA network in either hierarchical or recursive structure is suitable for a large number of competitors. The complexity and convergence performances of layer and direct WTA neural networks are analyzed. Simulation results and theoretical analyzes verify that the layer WTA neural networks with extendibility outperform their original direct WTA structures in aspects of low complexity and fast convergence.