Global Robust Exponential Stability of Interval Neural Networks with Delays
Neural Processing Letters
On Robust Exponential Periodicity of Interval Neural Networks with Delays
Neural Processing Letters
An analysis for periodic solutions of high-order BAM neural networks with delays
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Robust stability for interval Hopfield neural networks with time delay
IEEE Transactions on Neural Networks
Exponential stability and periodic oscillatory solution in BAM networks with delays
IEEE Transactions on Neural Networks
Neural Processing Letters
Robust Stability in Cohen---Grossberg Neural Network with both Time-Varying and Distributed Delays
Neural Processing Letters
Improved Global Robust Stability for Interval-Delayed Hopfield Neural Networks
Neural Processing Letters
Neural Processing Letters
Leakage Delays in T---S Fuzzy Cellular Neural Networks
Neural Processing Letters
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In this paper, a class of interval general bidirectional associative memory (BAM) neural networks with delays are introduced and studied, which include many well-known neural networks as special cases. By using fixed point technic, we prove an existence and uniqueness of the equilibrium point for the interval general BAM neural networks with delays. By using a proper Lyapunov functions, we get a sufficient condition to ensure the global robust exponential stability for the interval general BAM neural networks with delays, and we just require that activation function is globally Lipschitz continuous, which is less conservative and less restrictive than the monotonic assumption in previous results. In the last section, we also give an example to demonstrate the validity of our stability result for interval neural networks with delays.