scholarly journals Optical implementations of the alternating projection neural network

1991 ◽  
Vol 30 (10) ◽  
pp. 1522 ◽  
Author(s):  
Alan T. Smith
Keyword(s):  
Neural Network ◽  
Alternating Projection ◽  
Projection Neural Network

An Extended Projection Neural Network for Constrained Optimization

2004 ◽  
Vol 16 (4) ◽  
pp. 863-883 ◽  
Author(s):  
Youshen Xia
Keyword(s):  
Neural Network ◽  
Variational Inequality ◽  
Parallel Implementation ◽  
Variational Inequality Problems ◽  
Extended Projection ◽  
Linear And Nonlinear Constraints ◽  
Lower Complexity ◽  
Monotone Variational Inequality ◽  
Projection Neural Network ◽  
Special Case

Recently, a projection neural network has been shown to be a promising computational model for solving variational inequality problems with box constraints. This letter presents an extended projection neural network for solving monotone variational inequality problems with linear and nonlinear constraints. In particular, the proposed neural network can include the projection neural network as a special case. Compared with the modified projection-type methods for solving constrained monotone variational inequality problems, the proposed neural network has a lower complexity and is suitable for parallel implementation. Furthermore, the proposed neural network is theoretically proven to be exponentially convergent to an exact solution without a Lipschitz condition. Illustrative examples show that the extended projection neural network can be used to solve constrained monotone variational inequality problems.

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