New delay-dependent exponential stability criteria of BAM neural networks with time delays
Mathematics and Computers in Simulation
Novel delay-dependent global asymptotic stability condition of Hopfield neural networks with delays
Computers & Mathematics with Applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Global Asymptotic Stability of Cohen-Grossberg Neural Networks with Multiple Discrete Delays
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Scalability in evolved neurocontrollers that guide a swarm of robots in a navigation task
SAB'06 Proceedings of the 2nd international conference on Swarm robotics
Robust exponential stability analysis for unertain neural networks with time delay
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Robust stability for delayed neural networks with nonlinear perturbation
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Convergence analysis of genetic regulatory networks based on nonlinear measures
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
New results for global exponential stability of delayed cohen-grossberg neural networks
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Stability analysis for discrete-time Markovian jump neural networks with mixed time-delays
Expert Systems with Applications: An International Journal
Hopfield neural networks with unbounded monotone activation functions
Advances in Artificial Neural Systems
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In this paper, a new concept called nonlinear measure is introduced to quantify stability of nonlinear systems in the way similar to the matrix measure for stability of linear systems. Based on the new concept, a novel approach for stability analysis of neural networks is developed. With this approach, a series of new sufficient conditions for global and local exponential stability of Hopfield type neural networks is presented, which generalizes those existing results. By means of the introduced nonlinear measure, the exponential convergence rate of the neural networks to stable equilibrium point is estimated, and, for local stability, the attraction region of the stable equilibrium point is characterized. The developed approach can be generalized to stability analysis of other general nonlinear systems