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首页> 外文期刊>IEEE transactions on industrial informatics >A Variable-Gain Finite-Time Convergent Recurrent Neural Network for Time-Variant Quadratic Programming With Unknown Noises Endured
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A Variable-Gain Finite-Time Convergent Recurrent Neural Network for Time-Variant Quadratic Programming With Unknown Noises Endured

机译：具有未知噪声的时变二次规划的可变增益有限时间收敛递归神经网络

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摘要

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.
机译：首次提出了时变凸二次规划（QP）可变增益有限时间收敛和持久噪声归零神经网络（VGFTNE-ZNN）。与现有的具有恒定或可变设计增益（例如CGFT-ZNN和VGFT-ZNN）的有限时间收敛ZNN（噪声处理能力有限）不同，建议的VGFTNE-ZNN可通过动态调整其设计增益来承受加性噪声。有限的时间。解决QP问题时，允许无污染的VGFTNE-ZNN的设计增益保持恒定，而当时间发展到无穷大时，现有的无污染的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成功处理了更多实用的未知噪声。基于李雅普诺夫理论，对VGFTNE-ZNN的有限时间收敛性和抗噪性能进行了数学证明。相对于现有的CGFT-ZNN和VGFT-ZNN，VGFTNE-ZNN的优越性得到了比较，从而进行了数值验证。

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