Bidirectional associative memories
IEEE Transactions on Systems, Man and Cybernetics
Neural networks for control systems: a survey
Automatica (Journal of IFAC)
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: 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 delayed projection neural network for solving linear variational inequalities
IEEE Transactions on Neural Networks
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
Modeling, identification, and control of a class of nonlinear systems
IEEE Transactions on Fuzzy Systems
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
Recurrent correlation associative memories
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A Simplified Dual Neural Network for Quadratic Programming With Its KWTA Application
IEEE Transactions on Neural Networks
Solving Quadratic Programming Problems by Delayed Projection Neural Network
IEEE Transactions on Neural Networks
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There have been numerous recurrent neural network models diversely developed for modeling or simulating the associative memory behavior of human beings in the past decades, and the existing results for each model individual are in certain sense redundant with similarity. By utilizing the innate character of general activation operators, i.e., the uniformly pseudo-projection-anti-monotone property, a unified continuous-time recurrent neural network model is introduced, which can jointly cover almost all of the known continuous-time recurrent neural network individuals. Under the critical condition which is the intrinsic bounded line of stability and instability, we develop some convergence and stability theory for the unified recurrent neural network model when the time is continuous. The study shows that the approach adopted in the present paper is powerful, particularly in the sense of unifying, simplifying and extending the currently existing various models and dynamics results of continuous-time RNNs.