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• W3201957424 abstract "A new projection neural network approach is presented for the linear and convex quadratic second-order cone programming. In the method, the optimal conditions of the linear and convex second-order cone programming are equivalent to the cone projection equations. A Lyapunov function is given based on the G-norm distance function. Based on the cone projection function, the descent direction of Lyapunov function is used to design the new projection neural network. For the proposed neural network, we give the Lyapunov stability analysis and prove the global convergence. Finally, some numerical examples and two kinds of grasping force optimization problems are used to test the efficiency of the proposed neural network. The simulation results show that the proposed neural network is efficient for solving some linear and convex quadratic second-order cone programming problems. Especially, the proposed neural network can overcome the oscillating trajectory of the exist projection neural network for some linear second-order cone programming examples and the min-max grasping force optimization problem." @default.
• W3201957424 created "2021-10-11" @default.
• W3201957424 creator A5038791055 @default.
• W3201957424 creator A5058394999 @default.
• W3201957424 date "2021-10-04" @default.
• W3201957424 modified "2023-09-27" @default.
• W3201957424 title "A new projection neural network for linear and convex quadratic second-order cone programming" @default.
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• W3201957424 doi "https://doi.org/10.3233/jifs-210164" @default.
• W3201957424 hasPublicationYear "2021" @default.
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