On RNN-Based k-WTA Models With Time-Dependent Inputs  

作　　者：Mei Liu Mingsheng Shang 

机构地区：[1]the Chongqing Key Laboratory of Big Data and Intelligent Computing,Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Sciences,Chongqing 400714 [2]the Chongqing School,University of Chinese Academy of Sciences,Chongqing 400714,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2022年第11期2034-2036,共3页自动化学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(62072429);the Key Cooperation Project of Chongqing Municipal Education Commission(HZ2021017,HZ2021008)。

摘　　要：Dear editor,This letter identifies two weaknesses of state-of-the-art k-winnerstake-all(k-WTA)models based on recurrent neural networks(RNNs)when considering time-dependent inputs,i.e.,the lagging error and the infeasibility in finite-time convergence based on the Lipschitz continuity.

关 键 词：LETTER CONVERGENCE FINITE 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]