New zeroing neural network with finite-time convergence for dynamic complex-value linear equation and its applications

Guancheng Wang, Qinrou Li, Shaoqing Liu, Hua Xiao and Bob Zhang

Chaos, Solitons & Fractals, 2022, vol. 164, issue C

Abstract: This paper proposes a new zeroing neural network (NZNN) for solving the dynamic complex value linear equation (DCVLE). To achieve a faster convergence rate and improve the feasibility of the ZNN model, a bounded nonlinear mapping function is designed that endowed the NZNN model with finite-time convergence. Furthermore, regarding the different forms of the DCVLE in the Cartesian complex plane and the polar complex plane, two distinct NZNN models are proposed. In addition, the global convergence and the finite-time convergence of the NZNN models are analyzed and demonstrated by numerical simulations. Lastly, the NZNN model is successfully applied to the acoustic location and the control of a robotic manipulator, which well demonstrates its feasibility and efficiency.

Keywords: Zeroing neural network; Dynamic complex value linear equation; Bounded nonlinear activation function; Convergence analysis (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:164:y:2022:i:c:s0960077922008530

DOI: 10.1016/j.chaos.2022.112674

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