Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
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
Global Robust Exponential Stability of Interval General BAM Neural Network with Delays
Neural Processing Letters
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
Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays
IEEE Transactions on Neural Networks
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
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In this paper, a class of interval bidirectional associative memory (BAM) neural networks with mixed delays under uncertainty are introduced and studied, which include many well-known neural networks as special cases. The mixed delays mean the simultaneous presence of both the discrete delay, and the distributive delay. Furthermore, the parameter of matrix is taken values in a interval and controlled by a unknown, but bounded function. By using a suitable Lyapunov---Krasovskii function with the linear matrix inequality (LMI) technique, we obtain a sufficient condition to ensure the global robust exponential stability for the interval BAM neural networks with mixed delays under uncertainty, which is more generalized and less conservative, restrictive than previous results. In the last section, the validity of our stability result is demonstrated by a numerical example.