Global Exponential Stability of Periodic Solution to Cohen-Grossberg Type Neural Network with Both Ordinary and Neutral Type Discrete Time Varying Delays: An LMI Approach
نویسندگان
چکیده
A generalized Cohen-Grossberg neural network with both ordinary and neutral type discrete time varying delays is considered here. Behaved functions are not required to be differentiable. Sufficient conditions are presented for the uniqueness of periodic solution. By using Lyapunov stability theory and Linear Matrix Inequality (LMI) technique sufficient conditions are derived to ascertain the global exponential stability of periodic solution. Results are also verified by solving a suitable General Eigen Value Problem (GEVP). A numerical example is given to verify the obtained results.
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Global Robust Exponential Stability of Periodic Solution for Cohen-grossberg Type Neural Networks with Both Discrete and Neutral Time Varying Delays
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