首页|A Finite-Time Recurrent Neural Network for Computing Quadratic Minimization with Time-Varying Coefficients

A Finite-Time Recurrent Neural Network for Computing Quadratic Minimization with Time-Varying Coefficients

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This paper proposes a Finite-time Zhang neural network (FTZNN) to solve time-varying quadratic minimization problems. Different from the original Zhang neural network (ZNN) that is specially designed to solve time-varying problems and possesses an exponential convergence property, the proposed neural network exploits a sign-bi-power activation function so that it can achieve the finite-time convergence. In addition, the upper bound of the finite convergence time for the FTZNN model is analytically estimated in theory. For comparative purposes, the original ZNN model is also presented to solve time-varying quadratic minimization problems. Numerical experiments are performed to evaluate and compare the performance of the original ZNN model and the FTZNN model. The results demonstrate that the FTZNN model is a more effective solution model for solving time-varying quadratic minimization problems.

Recurrent neural networkQuadratic minimizationActivation functionFinite-time conver-genceComparative verification

XIAO Lin、LU Rongbo

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College of Information Science and Engineering, Jishou University, Jishou 416000, China

This work is supported by the National Natural Science Foundation of ChinaThis work is supported by the National Natural Science Foundation of ChinaThis work is supported by the National Natural Science Foundation of ChinaThis work is supported by the National Natural Science Foundation of ChinaThis work is supported by the National Natural Science Foundation of ChinaNatural Science Foundation of Hunan Province,ChinaNatural Science Foundation of Hunan Province,ChinaResearch Foundation of Education Bureau of Hunan Province,China

61866013No.61503152No.61563017No.61662025No.613630332019JJ50478No.2016JJ210115B192

2019

10.1049/cje.2019.01.009

中国电子杂志(英文版)

中国电子杂志(英文版)

CSTPCDCSCDSCIEI
ISSN：1022-4653
年,卷(期)：2019.28(2)
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