Recurrent Neural Network for Non-Smooth Convex Optimization Problems With Application to the Identification of Genetic Regulatory Networks

CORC > 自动化研究所 > 中国科学院自动化研究所 > 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

	Recurrent Neural Network for Non-Smooth Convex Optimization Problems With Application to the Identification of Genetic Regulatory Networks
	Cheng, Long1 ; Hou, Zeng-Guang1 ; Lin, Yingzi 2; Tan, Min1 ; Zhang, Wenjun Chris 3; Wu, Fang-Xiang 3
刊名	IEEE TRANSACTIONS ON NEURAL NETWORKS
	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
内容类型	期刊论文
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.