Bidirectional associative memories
IEEE Transactions on Systems, Man and Cybernetics
Convergent activation dynamics in continuous time networks
Neural Networks
Backpropagation in perceptrons with feedback
Neural Computers
Nonlinear differential equations and dynamical systems
Nonlinear differential equations and dynamical systems
Introduction to the theory of neural computation
Introduction to the theory of neural computation
On the brain-state-in-a-convex-domain neural models
Neural Networks
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
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Cellular Neural Networks
A reference model approach to stability analysis of neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Stability analysis of dynamical neural networks
IEEE Transactions on Neural Networks
A new neural network for solving linear and quadratic programming problems
IEEE Transactions on Neural Networks
A general methodology for designing globally convergent optimization neural networks
IEEE Transactions on Neural Networks
Stability analysis of Hopfield-type neural networks
IEEE Transactions on Neural Networks
Estimate of exponential convergence rate and exponential stability for 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
Stability of asymmetric Hopfield networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A new neural network for solving nonlinear projection equations
Neural Networks
The Dahlquist Constant Approach to Stability Analysis of the Static Neural Networks
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Delay-dependent globally exponential stability criteria for static neural networks: an LMI approach
IEEE Transactions on Circuits and Systems II: Express Briefs
Delay-dependent stability criterion of delayed recurrent neural networks via LMI approach
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Global exponential stability analysis for recurrent neural networks with time-varying delay
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
IEEE Transactions on Circuits and Systems Part I: Regular Papers
New stability criteria for recurrent neural networks with a time-varying delay
International Journal of Automation and Computing
Global asymptotic robust stability of static neural network models with S-type distributed delays
Mathematical and Computer Modelling: An International Journal
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The neuron state modeling and the local field modeling provides two fundamental modeling approaches to neural network research, based on which a neural network system can be called either as a static neural network model or as a local field neural network model. These two models are theoretically compared in terms of their trajectory transformation property, equilibrium correspondence property, nontrivial attractive manifold property, global convergence as well as stability in many different senses. The comparison reveals an important stability invariance property of the two models in the sense that the stability (in any sense) of the static model is equivalent to that of a subsystem deduced from the local field model when restricted to a specific manifold. Such stability invariance property lays a sound theoretical foundation of validity of a useful, cross-fertilization type stability analysis methodology for various neural network models.