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Global exponential stability of periodic solutions for Cohen-Grossberg neural networks involving generalized
Kuo-Shou Chiu1, Jyh-Cheng Jeng2, Tongxing Li3
1Departamento de Matemática, Facultad de Ciencias Básicas, Universidad Metropolitana de Ciencias de la Educación, José Pedro Alessandri 774, Santiago, Chile.
Abstract:
This paper investigates the global exponential stability and periodicity of the Cohen-Grossberg neural network model with generalized piecewise constant delay. By applying Schaefer's fixed-point theorem, a sufficient condition for the existence of periodic solutions in the model is established. Additionally, by constructing appropriate differential inequalities with generalized piecewise constant delay, sufficient conditions for the global exponential stability of the model are obtained. Finally, computer simulations are conducted to illustrate a globally exponentially stable periodic Cohen-Grossberg neural network model, thereby confirming the feasibility and effectiveness of the proposed results.
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