A Continuous Finite-time Neural Network with Bias Noises for Convex Quadratic Bilevel Programming Problem

A continuous finite-time neural network with bias noises is proposed to solve the convex quadratic bilevel programming problem in this paper. In order to solve the convex quadratic bilevel programming problem, it is transformed into a nonlinear programming problem based on the Kaeush-Kuhn-Tucker con...

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Bibliographic Details
Published in:International journal of control, automation, and systems 2022, Automation, and Systems, 20(9), , pp.3045-3052
Main Authors: Miao, Peng, Yang, Fan
Format: Article
Language:English
Subjects:
Bias
Control
Engineering
Kuhn-Tucker method
Mechatronics
Neural networks
Nonlinear programming
Regular Paper
Robotics
Robustness (mathematics)
Upper bounds
제어계측공학
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Summary:A continuous finite-time neural network with bias noises is proposed to solve the convex quadratic bilevel programming problem in this paper. In order to solve the convex quadratic bilevel programming problem, it is transformed into a nonlinear programming problem based on the Kaeush-Kuhn-Tucker conditions. Then, a neural network is designed to solve this problem. Compared with the existing networks, the designed network contains biased noise. Furthermore, it is proved that the proposed neural network can converge to the equilibrium point in finite time and it is Lyapunov stable. Moreover, the robustness performance of the present neural network against bias noises is discussed and the effect is very good. At the same time, the upper bound of the steady-state error is estimated. Lastly, two numerical examples show the effectiveness of the proposed methods.
ISSN:1598-6446
2005-4092
DOI:10.1007/s12555-021-0230-x