ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Using Neural Networks for the Foreign Investment Management Decision Support System
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Another Simple Recurrent Neural Network for Quadratic and Linear Programming
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
IEEE Transactions on Neural Networks
A delayed projection neural network for solving linear variational inequalities
IEEE Transactions on Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
On a Stabilization Problem of Nonlinear Programming Neural Networks
Neural Processing Letters
Design of recurrent neural networks for solving constrained least absolute deviation problems
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Generalized linear variational inequality (GLVI) is an extension of the canonical linear variational inequality. In recent years, a recurrent neural network (NN) called general projection neural network (GPNN) was developed for solving GLVIs with simple bound (often box-type or sphere-type) constraints. The aim of this paper is twofold. First, some further stability results of the GPNN are presented. Second, the GPNN is extended for solving GLVIs with general linear equality and inequality constraints. A new design methodology for the GPNN is then proposed. Furthermore, in view of different types of constraints, approaches for reducing the number of neurons of the GPNN are discussed, which results in two specific GPNNs. Moreover, some distinct properties of the resulting GPNNs are also explored based on their particular structures. Numerical simulation results are provided to validate the results.