A parameter-changing zeroing neural network for solving linear equations with superior fixed-time convergence

作者：

Highlights：

• A parameter-changing zeroing neural network is proposed for solving linear equations.

• The less conservative upper bound of the convergence time is calculated.

• Illustrative examples reveal that the proposed model is the best one.

• Synchronization experiments of chaotic systems verify the superiority of the model.

摘要

•A parameter-changing zeroing neural network is proposed for solving linear equations.•The less conservative upper bound of the convergence time is calculated.•Illustrative examples reveal that the proposed model is the best one.•Synchronization experiments of chaotic systems verify the superiority of the model.

论文关键词：Zeroing neural network,Linear equations,Parameter-changing,Fixed-time convergence,Chaotic systems

论文评审过程：Received 12 May 2020, Revised 1 July 2022, Accepted 4 July 2022, Available online 13 July 2022, Version of Record 19 July 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.118086