Recurrent Neural Network for Non-Smooth Convex Optimization Problems With Application to the Identification of Genetic Regulatory Networks
文献类型：期刊论文
| 作者 | Cheng, Long1 ; Hou, Zeng-Guang1; Lin, Yingzi2; Tan, Min1; Zhang, Wenjun Chris3; Wu, Fang-Xiang3
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| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS
 |
| 出版日期 | 2011-05-01 |
| 卷号 | 22期号:5页码:714-726 |
| 关键词 | Convex genetic regulatory network identification non-smooth optimization problem recurrent neural network |
| 英文摘要 | A recurrent neural network is proposed for solving the non-smooth convex optimization problem with the convex inequality and linear equality constraints. Since the objective function and inequality constraints may not be smooth, the Clarke's generalized gradients of the objective function and inequality constraints are employed to describe the dynamics of the proposed neural network. It is proved that the equilibrium point set of the proposed neural network is equivalent to the optimal solution of the original optimization problem by using the Lagrangian saddle-point theorem. Under weak conditions, the proposed neural network is proved to be stable, and the state of the neural network is convergent to one of its equilibrium points. Compared with the existing neural network models for non-smooth optimization problems, the proposed neural network can deal with a larger class of constraints and is not based on the penalty method. Finally, the proposed neural network is used to solve the identification problem of genetic regulatory networks, which can be transformed into a non-smooth convex optimization problem. The simulation results show the satisfactory identification accuracy, which demonstrates the effectiveness and efficiency of the proposed approach. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
| 研究领域[WOS] | Computer Science ; Engineering |
| 关键词[WOS] | NONLINEAR-PROGRAMMING PROBLEMS ; VARIATIONAL-INEQUALITIES ; COMPOUND-MODE ; SYSTEMS ; MANIPULATORS ; STABILITY ; SUBJECT |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000290414400004 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3455]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Key Lab Complex Syst & Intelligence Sci, Beijing 100190, Peoples R China 2.Northeastern Univ, Coll Engn, Mech & Ind Engn Dept, Boston, MA 02115 USA 3.Univ Saskatchewan, Dept Mech Engn, Saskatoon, SK S7N 5A9, Canada |
| 推荐引用方式 GB/T 7714 | Cheng, Long,Hou, Zeng-Guang,Lin, Yingzi,et al. Recurrent Neural Network for Non-Smooth Convex Optimization Problems With Application to the Identification of Genetic Regulatory Networks[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS,2011,22(5):714-726. |
| APA | Cheng, Long,Hou, Zeng-Guang,Lin, Yingzi,Tan, Min,Zhang, Wenjun Chris,&Wu, Fang-Xiang.(2011).Recurrent Neural Network for Non-Smooth Convex Optimization Problems With Application to the Identification of Genetic Regulatory Networks.IEEE TRANSACTIONS ON NEURAL NETWORKS,22(5),714-726. |
| MLA | Cheng, Long,et al."Recurrent Neural Network for Non-Smooth Convex Optimization Problems With Application to the Identification of Genetic Regulatory Networks".IEEE TRANSACTIONS ON NEURAL NETWORKS 22.5(2011):714-726. |
入库方式： OAI收割
来源：自动化研究所
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