Recurrent Algorithms for Selecting the Maximum Input
Neural Processing Letters
COMAX: A Cooperative Method for Determining the Position of the Maxima
Neural Processing Letters
Generalized hamming networks and applications
Neural Networks
Neural Computation
Dynamic analysis of a general class of winner-take-all competitive neural networks
IEEE Transactions on Neural Networks
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In this paper, a new iterative winner-take-all (WTA) neural network is developed and analyzed. The proposed WTA neural net with one-layer structure is established under the concept of the statistical mean. For three typical distributions of initial activations, the convergence behaviors of the existing and the proposed WTA neural nets are evaluated by theoretical analyses and Monte Carlo simulations. We found that the suggested WTA neural network on average requires fewer than Log2M iterations to complete a WTA process for the three distributed inputs, where M is the number of competitors. Furthermore, the fault tolerances of the iterative WTA nets are analyzed and simulated. From the view points of convergence speed, hardware complexity, and robustness to the errors, the proposed WTA is suitable for various applications