Convergent activation dynamics in continuous time networks
Neural Networks
Discrete state neural networks and energies
Neural Networks
Passive learning and input-to-state stability of switched Hopfield neural networks with time-delay
Information Sciences: an International Journal
Computers & Mathematics with Applications
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This paper presents new necessary and sufficient conditions for absolute stability of asymmetric neural networks. The main result is based on a solvable Lie algebra condition, which generalizes existing results for symmetric and normal neural networks. An exponential convergence estimate of the neural networks is also obtained. Further, it is demonstrated how to generate larger sets of weight matrices for absolute stability of the neural networks from known normal weight matrices through simple procedures. The approach is nontrivial in the sense that non-normal matrices can possibly be contained in the resulting weight matrix set. And the results also provide finite checking for robust stability of neural networks in the presence of parameter uncertainties.