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• W2049852972 endingPage "245202" @default.
• W2049852972 startingPage "245202" @default.
• W2049852972 abstract "A general type of recurrent neural network (termed as Zhang neural network, ZNN) has recently been proposed by Zhang et al for the online solution of time-varying quadratic-minimization (QM) and quadratic-programming (QP) problems. Global exponential convergence of the ZNN could be achieved theoretically in an ideal error-free situation. In this paper, with the normal differentiation and dynamics-implementation errors considered, the robustness properties of the ZNN model are investigated for solving these time-varying problems. In addition, linear activation functions and power-sigmoid activation functions could be applied to such a perturbed ZNN model. Both theoretical-analysis and computer-simulation results demonstrate the good ZNN robustness and superior performance for online time-varying QM and QP problem solving, especially when using power-sigmoid activation functions." @default.
• W2049852972 created "2016-06-24" @default.
• W2049852972 creator A5002078531 @default.
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• W2049852972 date "2010-05-19" @default.
• W2049852972 modified "2023-10-16" @default.
• W2049852972 title "Robustness analysis of the Zhang neural network for online time-varying quadratic optimization" @default.
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• W2049852972 doi "https://doi.org/10.1088/1751-8113/43/24/245202" @default.
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