Liu Qingshan, Dang Chuangyin, Cao Jinde
School of Automation, Southeast University, Nanjing 210096, China.
IEEE Trans Neural Netw. 2010 Jul;21(7):1140-8. doi: 10.1109/TNN.2010.2050781.
In this paper, based on a one-neuron recurrent neural network, a novel k-winners-take-all ( k -WTA) network is proposed. Finite time convergence of the proposed neural network is proved using the Lyapunov method. The k-WTA operation is first converted equivalently into a linear programming problem. Then, a one-neuron recurrent neural network is proposed to get the kth or (k+1)th largest inputs of the k-WTA problem. Furthermore, a k-WTA network is designed based on the proposed neural network to perform the k-WTA operation. Compared with the existing k-WTA networks, the proposed network has simple structure and finite time convergence. In addition, simulation results on numerical examples show the effectiveness and performance of the proposed k-WTA network.
本文基于单神经元递归神经网络，提出了一种新型的胜者全得（k-WTA）网络。利用李雅普诺夫方法证明了所提神经网络的有限时间收敛性。首先将k-WTA运算等效转化为一个线性规划问题。然后，提出一种单神经元递归神经网络来获取k-WTA问题中第k个或第（k + 1）个最大输入。此外，基于所提神经网络设计了一个k-WTA网络来执行k-WTA运算。与现有的k-WTA网络相比，所提网络结构简单且具有有限时间收敛性。此外，数值算例的仿真结果表明了所提k-WTA网络的有效性和性能。
