On the brain-state-in-a-convex-domain neural models
Neural Networks
Global attractivity in delayed Hopfield neural network models
SIAM Journal on Applied Mathematics
On the stability of globally projected dynamical systems
Journal of Optimization Theory and Applications
New theorems on global convergence of some dynamical systems
Neural Networks
Cellular neural networks and visual computing: foundations and applications
Cellular neural networks and visual computing: foundations and applications
Cellular Neural Networks
Cellular Neural Networks: Dynamics and Modelling (Mathematical Modelling: Theory and Applications)
Cellular Neural Networks: Dynamics and Modelling (Mathematical Modelling: Theory and Applications)
New Critical Analysis on Global Convergence of Recurrent Neural Networks with Projection Mappings
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
A reference model approach to stability analysis of neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Stability analysis of dynamical neural networks
IEEE Transactions on Neural Networks
On equilibria, stability, and instability of Hopfield neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Global convergence of delayed dynamical systems
IEEE Transactions on Neural Networks
Solving Quadratic Programming Problems by Delayed Projection Neural Network
IEEE Transactions on Neural Networks
Neural network for quadratic optimization with bound constraints
IEEE Transactions on Neural Networks
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Dynamics research of recurrent neural networks is very meaningful in both theoretical importance and practical significance. Recently, the study on the critical dynamics behaviors of such networks has drawn especial attention because of its application requirements. In this paper, new criteria are found to ascertain the global convergence and asymptotic stability of recurrent neural networks under the generally P-critical conditions, i.e., a discriminant matrix M(L,@C)+P is nonnegative definite, where M(L,@C) is a matrix related with the network and P is an arbitrary nonnegative definite matrix. The analysis results given in this paper improve substantially upon the existing relevant convergence and stability results in literature, including both the non-critical conclusions, i.e., the dynamics analysis under the conditions that M(L,@C) is positive definite, and the special critical discuss when M(L,@C) is nonnegative definite.