One-Layer Continuous-and Discrete-Time Projection Neural Networks for Solving Variational Inequalities and Related Optimization Problems
Title | One-Layer Continuous-and Discrete-Time Projection Neural Networks for Solving Variational Inequalities and Related Optimization Problems |
Publication Type | Journal Article |
Year of Publication | 2014 |
Authors | Qingshan Liu, Tingwen Huang, Jun Wang |
Journal | Neural Networks and Learning Systems, IEEE Transactions on |
Volume | 25 |
Pagination | 1308-1318 |
Date Published | July |
ISSN | 2162-237X |
Keywords | constrained optimization, constrained variational inequalities, convergence, discrete time systems, Educational institutions, global convergence, Lyapunov methods, Lyapunov stability, Mathematical model, neural nets, Neural networks, one-layer continuous-time projection neural networks, one-layer discrete-time projection neural networks, optimisation, Optimization, optimization problems, projection neural network, sufficient conditions, variational inequalities, variational inequalities., variational techniques, Vectors |
Abstract | This paper presents one-layer projection neural networks based on projection operators for solving constrained variational inequalities and related optimization problems. Sufficient conditions for global convergence of the proposed neural networks are provided based on Lyapunov stability. Compared with the existing neural networks for variational inequalities and optimization, the proposed neural networks have lower model complexities. In addition, some improved criteria for global convergence are given. Compared with our previous work, a design parameter has been added in the projection neural network models, and it results in some improved performance. The simulation results on numerical examples are discussed to demonstrate the effectiveness and characteristics of the proposed neural networks. |
DOI | 10.1109/TNNLS.2013.2292893 |
Citation Key | 6680760 |
- one-layer continuous-time projection neural networks
- Vectors
- variational techniques
- variational inequalities.
- variational inequalities
- sufficient conditions
- projection neural network
- optimization problems
- optimization
- optimisation
- one-layer discrete-time projection neural networks
- constrained optimization
- Neural networks
- neural nets
- Mathematical model
- Lyapunov stability
- Lyapunov methods
- global convergence
- Educational institutions
- discrete time systems
- convergence
- constrained variational inequalities