Robust Linear Neural Network for Constrained Quadratic Optimization
Based on the feature of projection operator under box constraint, by using convex analysis method, this paper proposed three robust linear systems to solve a class of quadratic optimization problems. Utilizing linear matrix inequality (LMI) technique, eigenvalue perturbation theory, Lyapunov-Razumik...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2017/5073640 |
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| _version_ | 1832562564019519488 |
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| author | Zixin Liu Yuanan Liu Lianglin Xiong |
| author_facet | Zixin Liu Yuanan Liu Lianglin Xiong |
| author_sort | Zixin Liu |
| collection | DOAJ |
| description | Based on the feature of projection operator under box constraint, by using convex analysis method, this paper proposed three robust linear systems to solve a class of quadratic optimization problems. Utilizing linear matrix inequality (LMI) technique, eigenvalue perturbation theory, Lyapunov-Razumikhin method, and LaSalle’s invariance principle, some stable criteria for the related models are also established. Compared with previous criteria derived in the literature cited herein, the stable criteria established in this paper are less conservative and more practicable. Finally, a numerical simulation example and an application example in compressed sensing problem are also given to illustrate the validity of the criteria established in this paper. |
| format | Article |
| id | doaj-art-cb3341f25beb4363b70261379326044b |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-cb3341f25beb4363b70261379326044b2025-02-03T01:22:24ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2017-01-01201710.1155/2017/50736405073640Robust Linear Neural Network for Constrained Quadratic OptimizationZixin Liu0Yuanan Liu1Lianglin Xiong2School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 102209, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 102209, ChinaSchool of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650500, ChinaBased on the feature of projection operator under box constraint, by using convex analysis method, this paper proposed three robust linear systems to solve a class of quadratic optimization problems. Utilizing linear matrix inequality (LMI) technique, eigenvalue perturbation theory, Lyapunov-Razumikhin method, and LaSalle’s invariance principle, some stable criteria for the related models are also established. Compared with previous criteria derived in the literature cited herein, the stable criteria established in this paper are less conservative and more practicable. Finally, a numerical simulation example and an application example in compressed sensing problem are also given to illustrate the validity of the criteria established in this paper.http://dx.doi.org/10.1155/2017/5073640 |
| spellingShingle | Zixin Liu Yuanan Liu Lianglin Xiong Robust Linear Neural Network for Constrained Quadratic Optimization Discrete Dynamics in Nature and Society |
| title | Robust Linear Neural Network for Constrained Quadratic Optimization |
| title_full | Robust Linear Neural Network for Constrained Quadratic Optimization |
| title_fullStr | Robust Linear Neural Network for Constrained Quadratic Optimization |
| title_full_unstemmed | Robust Linear Neural Network for Constrained Quadratic Optimization |
| title_short | Robust Linear Neural Network for Constrained Quadratic Optimization |
| title_sort | robust linear neural network for constrained quadratic optimization |
| url | http://dx.doi.org/10.1155/2017/5073640 |
| work_keys_str_mv | AT zixinliu robustlinearneuralnetworkforconstrainedquadraticoptimization AT yuananliu robustlinearneuralnetworkforconstrainedquadraticoptimization AT lianglinxiong robustlinearneuralnetworkforconstrainedquadraticoptimization |