Stability analysis of time-varying delay neural network for convex quadratic programming with equality constraints and inequality constraints. (English) Zbl 1528.34056
Summary: This paper presented a class of neural networks with time-varying delays to solve quadratic programming problems. Compared with previous papers, the neural networks proposed in this paper replaced the constant time delays \(\tau \) with variable time delays \(\tau(t) \) and had a more concise structure. There was an improvement of previous method in proving the existence and uniqueness of solutions of the neural networks in this paper. Further, this paper gave the conditions to be satisfied for the global exponential stability of the proposed neural networks. Through numerical examples, this paper verified that the proposed neural networks were accurate and efficient in solving the quadratic programming problems.
MSC:
| 34K20 | Stability theory of functional-differential equations |
| 92B20 | Neural networks for/in biological studies, artificial life and related topics |
| 90C30 | Nonlinear programming |
Keywords:
neural network; global exponential stability; convex quadratic programming; time-varying delay; optimizationReferences:
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