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Robust synchronization of delayed neural networks based on adaptive control and parameters identification. (English) Zbl 1091.93032
Summary: This paper investigates synchronization dynamics of delayed neural networks with all the parameters unknown. By combining the adaptive control and linear feedback with the updated law, some simple yet generic criteria for determining the robust synchronization based on the parameters identification of uncertain chaotic delayed neural networks are derived by using the invariance principle of functional differential equations. It is shown that the approaches developed here further extend the ideas and techniques presented in recent literature, and they are also simple to implement in practice. Furthermore, the theoretical results are applied to a typical chaotic delayed Hopfied neural networks, and numerical simulation also demonstrate the effectiveness and feasibility of the proposed technique.
MSC:
93D15Stabilization of systems by feedback
34K20Stability theory of functional-differential equations
37N25Dynamical systems in biology
92B20General theory of neural networks (mathematical biology)