Nonconvex and Bound Constraint Zeroing Neural Network for Solving Time-Varying Complex-Valued Quadratic Programming Problem

Jiang, Chengze; Xiao, Xiuchun; Liu, Dazhao; Huang, Haoen; Xiao, Hua; Lu, Huosheng

doi:10.1109/tii.2020.3047959

Cited by 36 publications

(23 citation statements)

References 28 publications

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“…To better describe the performance of the NANSZNN model (15), comparative simulation experiments are conducted in this section. As the aforementioned discussions, the ZNN related models, such as OZNN model [9], NCZNN model [29], and PTCZNN model [19], have a benign performance in the TMSR-solving task (1). Thus, we compare these models with the proposed NANSZNN model.…”

Section: Simulationsmentioning

confidence: 99%

A Non-Convex Activation and Noise-Suppressing Zeroing Neural Network for Finding Time-Varying Matrix Square Root

et al. 2022

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In this paper, a zeroing neural network model with properties of non-convex activation and noise suppressing (NANSZNN) is proposed for finding the square root of a time-varying matrix. In comparison with the existing zeroing neural network models, the proposed NANSZNN model relieves the limit of convex constraint and makes a breakthrough in noise-suppressing. Furthermore, theoretical analyses and strict proofs are provided in detail for the global convergence and noise-suppressing performance of the proposed NANSZNN model under the circumstances of various noises by using Lyaponov stability theory. Finally, numerical experiments and comparative analyses are offered to further illustrate the availability, effectiveness, and robustness against different noises of the proposed NANSZNN model.

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Section: Simulationsmentioning

confidence: 99%

A Non-Convex Activation and Noise-Suppressing Zeroing Neural Network for Finding Time-Varying Matrix Square Root

et al. 2022

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“…• NCZNN model presented in [37]. Where the parameter R Υ (•) represents the non-convex and bounded activation function.…”

Section: A Time-dependent Situationmentioning

confidence: 99%

Adaptive Zeroing-Type Neural Dynamics for Solving Quadratic Minimization and Applied to Target Tracking

Jiang¹,

Jin²,

Xiao³

2021

Preprint

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The time-dependent quadratic minimization (TDQM) problem appears in many applications and research projects. It has been reported that the zeroing neural network (ZNN) models can effectively solve the TDQM problem. However, the convergent and robust performance of the existing ZNN models are restricted for lack of a joint-action mechanism of adaptive coefficient and integration enhanced term. Consequently, the residual-based adaption coefficient zeroing neural network (RACZNN) model with integration term is proposed in this paper for solving the TDQM problem. The adaptive coefficient is proposed to improve the performance of convergence and the integration term is embedded to ensure the RACZNN model can maintain reliable robustness while perturbed by variant measurement noises. Compared with the state-of-the-art models, the proposed RACZNN model owns faster convergence and more reliable robustness. Then, theorems are provided to prove the convergence of the RACZNN model. Finally, corresponding quantitative numerical experiments are designed and performed in this paper to verify the performance of the proposed RACZNN model.

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“…By making full use of the time derivative information of time-varying parameters, the theoretical solution to the timevarying problems is tracked by the evolution formula. Taking advantage of the method of ZNN [20]- [22], the objective of effectively solving time-varying problems is achieved. Specifically, Xu et al [23] furnish a ZNN model for ensuring the solution of the TVLEI problem, which takes full advantage of the ZNN model as well as the time-derivative information about the time-varying coefficients involved in the time-varying problems.…”

Section: Introductionmentioning

confidence: 99%

Solving Perturbed Time-Varying Linear Equation and Inequality Problem With Adaptive Enhanced and Noise Suppressing Zeroing Neural Network

Huang

et al. 2022

IEEE Access

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Solving time-varying linear equation and inequality (TVLEI) problem has attracted extensive attention in numerous scientific and engineered fields. In this article, it is basically considered that the commonly used dynamics neural network in the virtual environment is inevitably interfered with by the variable measurement noises while dealing with the TVLEI problem. An adaptive enhanced and noisesuppressing zeroing neural network (AENSZNN) model is proposed as an improved algorithm for solving the TVLEI problem. An adaptive scale factor based on the residual error norm is designed to make the proposed AENSZNN model converge to the theoretical solution faster. Furthermore, the momentum enhancement terms added to the model enables the AENSZNN model to effectively solve the TVLEI problem in real-time under the obstruction of different measurement noises. Besides, theoretical results and numerical experiments indicate that the AENSZNN model has advantages in convergence accuracy and robustness to noises compared with the existing algorithms. Note that, the proposed AENSZNN model is successfully exploited for the estimation of mobile object localization.

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Nonconvex and Bound Constraint Zeroing Neural Network for Solving Time-Varying Complex-Valued Quadratic Programming Problem

Cited by 36 publications

References 28 publications

A Non-Convex Activation and Noise-Suppressing Zeroing Neural Network for Finding Time-Varying Matrix Square Root

A Non-Convex Activation and Noise-Suppressing Zeroing Neural Network for Finding Time-Varying Matrix Square Root

Adaptive Zeroing-Type Neural Dynamics for Solving Quadratic Minimization and Applied to Target Tracking

Solving Perturbed Time-Varying Linear Equation and Inequality Problem With Adaptive Enhanced and Noise Suppressing Zeroing Neural Network

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