Improved finite-time solutions to time-varying Sylvester tensor equation via zeroing neural networks

作者：

Highlights：

• Finite-time solution of time-varying Sylvester tensor equation is studied for the first time.

• The upper bounds of convergence time for four zeroing neural network models are derived in theory.

• The correctness and superiority of the proposed method are demonstrated by two simulation examples.

摘要

•Finite-time solution of time-varying Sylvester tensor equation is studied for the first time.•The upper bounds of convergence time for four zeroing neural network models are derived in theory.•The correctness and superiority of the proposed method are demonstrated by two simulation examples.

论文关键词：Time-varying Sylvester tensor equation,Zeroing neural networks,Varying parameter,Finite-time convergence

论文评审过程：Received 6 May 2021, Revised 27 August 2021, Accepted 28 October 2021, Available online 10 November 2021, Version of Record 10 November 2021.

论文官网地址：https://doi.org/10.1016/j.amc.2021.126760