New results for neutral-type delayed projection neural network to solve linear variational inequalities

摘要

Recently, a neutral-type delayed projection neural network (NDPNN) was developed for solving variational inequality problems. This paper addresses the global stability and convergence of the NDPNN and presents new results for it to solve linear variational inequality (LVI). Compared with existing convergence results for neural networks to solve LVI, our results do not require the LVI that is monotone so as to guarantee the NDPNN that can solve a class of non-monotone LVI. All the results are expressed in terms of linear matrix inequalities, which can be easily checked. Simulation examples demonstrate the effectiveness of the obtained results.