A predefined-time and anti-noise varying-parameter ZNN model for solving time-varying complex Stein equations

Xiao, Lin; Li, Linju; Tao, Juan; Li, Weibing

doi:10.1016/j.neucom.2023.01.008

Cited by 22 publications

(2 citation statements)

References 26 publications

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“…With the rapid advancement of ZNN in the last decade, its impact has been notable across various fields. Aside from its application in robotics and chaotic systems as previously mentioned, ZNN has also been utilized in image information processing [38], [114], [115], multi-dimensional spectral estimation [67], mathematical ecology [3], pendulum tracking of IPC systems [116], mobile object localization [117]- [119], and other areas.…”

Section: Applications In Other Fieldsmentioning

confidence: 99%

Applications of Zeroing Neural Networks: A Survey

Wang,

Zhang,

Huang

et al. 2024

IEEE Access

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Time-varying problems are prevalent in engineering, presenting a significant challenge due to the fluctuations in parameters and goals at different time points. The Zeroing Neural Network (ZNN), a specialized form of Recurrent Neural Network (RNN) developed by Zhang et al., has gained attention for its rapid convergence speed and robustness making it a valuable tool for real-time solving of diverse timevarying problems. This review article explores the practical applications of ZNN across various domains in the past two decades, specifically focusing on robot manipulator path tracking, motion planning, and chaotic systems. The comprehensive scope of this review is essential for researchers and beginners looking to grasp the efficacy of ZNN in addressing practical challenges in diverse fields. INDEX TERMS time-varying problems, Zeroing neural network (ZNN),robustness,robot manipulator I. INTRODUCTION

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Section: Applications In Other Fieldsmentioning

confidence: 99%

Applications of Zeroing Neural Networks: A Survey

Wang,

Zhang,

Huang

et al. 2024

IEEE Access

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“…It also shows constant stability under noisy environments and can resist noise well. Xiao et al [30] designed a ZNN model that can maintain performance in a noisy environment, with appropriate variations in the parameters used to solve problems with unsteady parameters. Meanwhile, models also deal with the dynamic problems of complex dynamic Stein equations, and the method is applied to robot control.…”

Section: Introductionmentioning

confidence: 99%

Bounded Adaptive Function Activated Recurrent Neural Network for Solving the Dynamic QR Factorization

Yang

Xie

et al. 2023

Mathematics

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The orthogonal triangular factorization (QRF) method is a widespread tool to calculate eigenvalues and has been used for many practical applications. However, as an emerging topic, only a few works have been devoted to handling dynamic QR factorization (DQRF). Moreover, the traditional methods for dynamic problems suffer from lagging errors and are susceptible to noise, thereby being unable to satisfy the requirements of the real-time solution. In this paper, a bounded adaptive function activated recurrent neural network (BAFARNN) is proposed to solve the DQRF with a faster convergence speed and enhance existing solution methods’ robustness. Theoretical analysis shows that the model can achieve global convergence in different environments. The results of the systematic experiment show that the BAFARNN model outperforms both the original ZNN (OZNN) model and the noise-tolerant zeroing neural network (NTZNN) model in terms of accuracy and convergence speed. This is true for both single constants and time-varying noise disturbances.

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Efficient Prescribed-Time and Robust Zeroing Neural Networks for Computing Time-Variant Plural Stein Matrix Equation

Li,

2024

Lecture Notes in Computer Science

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A predefined-time and anti-noise varying-parameter ZNN model for solving time-varying complex Stein equations

Cited by 22 publications

References 26 publications

Applications of Zeroing Neural Networks: A Survey

Applications of Zeroing Neural Networks: A Survey

Bounded Adaptive Function Activated Recurrent Neural Network for Solving the Dynamic QR Factorization

Efficient Prescribed-Time and Robust Zeroing Neural Networks for Computing Time-Variant Plural Stein Matrix Equation

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