A neural network for constrained optimization with application to CDMA communication systems

Fantacci, R.; Forti, Mauro; Marini, Mauro; Tarchi, Daniele; Vannuccini, Gianluca

doi:10.1109/tcsii.2003.814805

Cited by 24 publications

(6 citation statements)

References 9 publications

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“…The adaptive coefficient (•) and scale coefficient k of the proposed AZNNNA model are set as ( e(t)) = || e(t)|| 3 2 + 5 and k = 5, respectively. The corresponding quantitative simulation results of the example are arranged in Figures 1-5.…”

Section: Time-varying Quadratic Minimization Examplementioning

confidence: 99%

“…Quadratic minimization (QM) is a widely studied branch of optimization theory, with applications in various fields such as image processing [1,2], communication engineering [3], robot kinematics [4], and energy system design [5]. While numerical algorithms can efficiently solve static QM problems, they are unable to handle large-scale time-varying quadratic minimization (TVQM) problems with real-time requirements due to their serial processing mechanism.…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Zeroing Neural Network with Non-Convex Activation for Time-Varying Quadratic Minimization

et al. 2023

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The field of position tracking control and communication engineering has been increasingly interested in time-varying quadratic minimization (TVQM). While traditional zeroing neural network (ZNN) models have been effective in solving TVQM problems, they have limitations in adapting their convergence rate to the commonly used convex activation function. To address this issue, we propose an adaptive non-convex activation zeroing neural network (AZNNNA) model in this paper. Using the Lyapunov theory, we theoretically analyze the global convergence and noise-immune characteristics of the proposed AZNNNA model under both noise-free and noise-perturbed scenarios. We also provide computer simulations to illustrate the effectiveness and superiority of the proposed model. Compared to existing ZNN models, our proposed AZNNNA model outperforms them in terms of efficiency, accuracy, and robustness. This has been demonstrated in the simulation experiment of this article.

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Section: Time-varying Quadratic Minimization Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Adaptive Zeroing Neural Network with Non-Convex Activation for Time-Varying Quadratic Minimization

et al. 2023

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“…T HE quadratic minimization (QM) as a branch of optimization theory has been extensively studied in controller design [1], communication engineering [2], energy system design [3], image processing [4], robot kinematics [5], and is still a very active research area [6], [7]. In summary, when facing general QM problems, the conventional scheme is to solve them with the help of some numerical or iterative algorithms [8].…”

Section: Introductionmentioning

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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“…It is usually an essential part of the solution of many problems, e.g. communication systems [1] , digital signal processing [2] , and robot-arm motion planning [3] . In addition, the quadratic minimization has great importance both from the mathematical and practical viewpoints, because a large number of methods have been proposed for solving quadratic optimization problems in real-time [3−6] .…”

Section: Introductionmentioning

confidence: 99%

“…(6) and FTZNN model Eq (9). have been presented for computing online time-varying quadratic minimization equation Eq (1)…”

mentioning

confidence: 99%

A Finite‐Time Recurrent Neural Network for Computing Quadratic Minimization with Time‐Varying Coefficients

Xiao

2019

Chin. j. electron.

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A neural network for constrained optimization with application to CDMA communication systems

Cited by 24 publications

References 9 publications

An Adaptive Zeroing Neural Network with Non-Convex Activation for Time-Varying Quadratic Minimization

An Adaptive Zeroing Neural Network with Non-Convex Activation for Time-Varying Quadratic Minimization

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

A Finite‐Time Recurrent Neural Network for Computing Quadratic Minimization with Time‐Varying Coefficients

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