The convergence property of discrete-time neural network for quadratic programming is analyzed. By choosing a proper Lyapunov function, a sufficient ondition for global convergence is obtained. The convergence rate under the condition is also investigated through a in-depth discussion about full-row-rank inequality constraint left matrix condition nd non-full-row-rank inequality constraint left matrix condition, respectively, and the exponential convergence property for both full-row rank and non-full-row rank inequality constraint left matrix conditions under the mentioned sufficient condition is proved. Simulation result
verifies the validity of the theoretical results obtained in this paper.
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