A new delayed projection neural network for solving quadratic programming problems with equality and inequality constraints

Sha, Chunlin; Zhao, Hongyong; Ren, Fengyuan

doi:10.1016/j.neucom.2015.05.006

Cited by 17 publications

(2 citation statements)

References 28 publications

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“…A projection neural network with two different time delays was proposed in [17] to better solve the quadratic programming problem. Neural networks with delay for solving strictly convex optimization problems were considered by Sha et al [22]. It is worth noting that the time delay in the neural networks studied in [12,22,17,3] is only a constant one, but in the real situation, it is the possibility that the time delay varies with time at different moments owing to the interference of voltage, capacitance, and other factors.…”

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confidence: 99%

“…Neural networks with delay for solving strictly convex optimization problems were considered by Sha et al [22]. It is worth noting that the time delay in the neural networks studied in [12,22,17,3] is only a constant one, but in the real situation, it is the possibility that the time delay varies with time at different moments owing to the interference of voltage, capacitance, and other factors. While, there is still few research on time-varying delay neural networks for solving convex quadratic programming problems.…”

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confidence: 99%

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Stability analysis of time-varying delay neural network for convex quadratic programming with equality constraints and inequality constraints

Zhang¹,

Sun²

2022

DCDS-B

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<p style='text-indent:20px;'>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 <inline-formula><tex-math id="M1">\begin{document}$ \tau $\end{document}</tex-math></inline-formula> with variable time delays <inline-formula><tex-math id="M2">\begin{document}$ \tau(t) $\end{document}</tex-math></inline-formula> 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.</p>

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confidence: 99%