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A One-Layer Recurrent Neural Network for Solving Pseudoconvex Optimization with Box Set Constraints
Joint Authors
Yao, Rong
Zhang, Xiaowei
Wu, Huaiqin
Li, Ruoxia
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-02-27
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
A one-layer recurrent neural network is developed to solve pseudoconvex optimization with box constraints.
Compared with the existing neural networks for solving pseudoconvex optimization, the proposed neural network has a wider domain for implementation.
Based on Lyapunov stable theory, the proposed neural network is proved to be stable in the sense of Lyapunov.
By applying Clarke’s nonsmooth analysis technique, the finite-time state convergence to the feasible region defined by the constraint conditions is also addressed.
Illustrative examples further show the correctness of the theoretical results.
American Psychological Association (APA)
Wu, Huaiqin& Yao, Rong& Li, Ruoxia& Zhang, Xiaowei. 2014. A One-Layer Recurrent Neural Network for Solving Pseudoconvex Optimization with Box Set Constraints. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-460163
Modern Language Association (MLA)
Wu, Huaiqin…[et al.]. A One-Layer Recurrent Neural Network for Solving Pseudoconvex Optimization with Box Set Constraints. Mathematical Problems in Engineering No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-460163
American Medical Association (AMA)
Wu, Huaiqin& Yao, Rong& Li, Ruoxia& Zhang, Xiaowei. A One-Layer Recurrent Neural Network for Solving Pseudoconvex Optimization with Box Set Constraints. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-460163
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-460163