A variable-gain finite-time convergent and noise-enduring zeroing neural network (VGFTNE-ZNN) is for the first time proposed for time-variant convex quadratic programming (QP). Differing from the existing finite-time convergent ZNNs with constant or variable design gains (i.e., CGFT-ZNN and VGFT-ZNN) that have limited noise-handling capabilities, the proposed VGFTNE-ZNN can endure additive noises by dynamically adjusting its design gains in finite time. Design gains of the unpolluted VGFTNE-ZNN are allowed to be constant when the QP problem is solved, whereas the design gain of the existing unpolluted VGFT-ZNN unrealistically increases to infinity when time evolves to infinity. Unlike existing polluted ZNNs with known noises involved, more practical unknown noises are successfully handled by the VGFTNE-ZNN. The finite-time convergence and noise-endurance properties of the VGFTNE-ZNN are mathematically proved based on the Lyapunov theory. Numerical verifications are comparatively performed with the superiorities of the VGFTNE-ZNN substantiated as compared with the existing CGFT-ZNN and VGFT-ZNN.

中文翻译：

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承受未知噪声时变二次规划的变增益有限时间收敛递归神经网络


首次提出用于时变凸二次规划（QP）的可变增益有限时间收敛和耐噪声归零神经网络（VGFTNE-ZNN）。与现有的具有恒定或可变设计增益的有限时间收敛 ZNN（即 CGFT-ZNN 和 VGFT-ZNN）的噪声处理能力有限不同，所提出的 VGFTNE-ZNN 可以通过动态调整其设计增益来承受加性噪声。有限的时间。当QP问题解决时，未污染的VGFTNE-ZNN的设计增益被允许保持恒定，而当时间演化到无穷大时，现有的未污染的VGFT-ZNN的设计增益不切实际地增加到无穷大。与涉及已知噪声的现有污染 ZNN 不同，VGFTNE-ZNN 成功处理了更实际的未知噪声。基于Lyapunov理论，数学证明了VGFTNE-ZNN的有限时间收敛性和抗噪性。通过数值验证，证实了VGFTNE-ZNN相对于现有的CGFT-ZNN和VGFT-ZNN的优越性。