Yang J F, Chen C M, Wang W C, Lee J Y
Dept. of Electr. Eng., Nat. Cheng Kung Univ., Tainan.
IEEE Trans Neural Netw. 1995;6(1):14-24. doi: 10.1109/72.363454.
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 Log(2)M 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.
本文开发并分析了一种新的迭代胜者全得（WTA）神经网络。所提出的具有单层结构的WTA神经网络是在统计均值的概念下建立的。对于初始激活的三种典型分布，通过理论分析和蒙特卡罗模拟评估了现有WTA神经网络和所提出的WTA神经网络的收敛行为。我们发现，对于三种分布式输入，所建议的WTA神经网络平均需要少于Log(2)M次迭代来完成一个WTA过程，其中M是竞争单元的数量。此外，还对迭代WTA网络的容错能力进行了分析和模拟。从收敛速度、硬件复杂度和对误差的鲁棒性来看，所提出的WTA适用于各种应用。
