A Delayed Projection Neural Network for Solving Linear Variational Inequalities
Long Cheng, Zeng‐Guang Hou, Min Han Tan · IEEE Transactions on Neural Networks · 2009
In this paper, a delayed projection neural network is proposed for solving a class of linear variational inequality problems. The theoretical analysis shows that the proposed neural network is globally exponentially stable under different conditions. By the proposed linear matrix inequality (LMI) method, the monotonicity assumption on the linear variational inequality is no longer necessary. By employing Lagrange multipliers, the proposed method can resolve the constrained quadratic programming problems. Finally, simulation examples are given to demonstrate the satisfactory performance of the proposed neural network.