CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
Solving convex optimization problems using recurrent neural networks in finite time
Long Cheng; Zeng-Guang Hou; Noriyasu Homma; Min Tan; Madan M. Gupta
2009
Conference NameInternational Joint Conference on Neural Networks
Conference DateJUN 14-19, 2009
Conference PlaceAtlanta
CountryUSA
AbstractA recurrent neural network is proposed to deal with the convex optimization problem. By employing a specific nonlinear unit, the proposed neural network is proved to be convergent to the optimal solution in finite time, which increases the computation efficiency dramatically. Compared with most of existing stability conditions, i.e., asymptotical stability and exponential stability, the obtained finite-time stability result is more attractive, and therefore could be considered as a useful supplement to the current literature. In addition, a switching structure is suggested to further speed up the neural network convergence. Moreover, by using the penalty function method, the proposed neural network can be extended straightforwardly to solving the constrained optimization problem. Finally, the satisfactory performance of the proposed approach is illustrated by two simulation examples.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23154
Collection复杂系统管理与控制国家重点实验室_先进机器人
Recommended Citation
GB/T 7714
Long Cheng,Zeng-Guang Hou,Noriyasu Homma,et al. Solving convex optimization problems using recurrent neural networks in finite time[C],2009.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Long Cheng]'s Articles
[Zeng-Guang Hou]'s Articles
[Noriyasu Homma]'s Articles
Baidu academic
Similar articles in Baidu academic
[Long Cheng]'s Articles
[Zeng-Guang Hou]'s Articles
[Noriyasu Homma]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Long Cheng]'s Articles
[Zeng-Guang Hou]'s Articles
[Noriyasu Homma]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.