时变二次规划的鲁棒归零神经网络求解模型

doi:10.12305/j.issn.1001-506X.2025.09.01

Abstract

Abstract:

To address the noise amplification problem caused by the differential operation in the discrete-time zeroing neural network solution model for time-varying quadratic programming, a robust discrete-time zeroing neural network solution model is proposed. Firstly, a state space model of the differential operation via a polynomial prediction filter is established. Secondly a robust differentiator is proposed by a Kalman filter robust to the observation noise. It is demonstrated that the impact of the noisy observations on the solution model can be minimized in the sense of maximum a posteriori probability when the proposed robust differentiator is employed to replace the differential operations in the solution model. Finally, the simulation experiments demonstrate that the proposed method gives superior performance compared to the existing discrete-time zeroing neural network solution models, especially in the presence of noisy observations.

Key words: time-varying quadratic programming （QP）, zeroing neural network, polynomial prediction filter, robust differentiator

CLC Number:

TP 183

Geng HUANG, Dan LI, Jianqiu ZHANG. Robust zeroing neural network solution model for time-varying quadratic programming[J]. Systems Engineering and Electronics, 2025, 47(9): 2775-2784.

Figures/Tables 9

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References 36

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模型	RMSE
DZNN求解式（12）	0.1176
DZNN求解式（13）	0.1273
RDZNN求解式（35）	0.0860
RDZNN求解式（36）	0.1381

模型	RMSE
DZNN求解式（12）	0.2516
DZNN求解式（13）	0.2948
RDZNN求解式（35）	0.1376
RDZNN求解式（36）	0.2127

模型	RMSE
模型	2Σ₁	4Σ₁	10Σ₁
RDZNN求解式（35）	0.1407	0.1403	0.1392
RDZNN求解式（36）	0.2045	0.2169	0.2132

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Robust zeroing neural network solution model for time-varying quadratic programming

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