An improved fixed-time stabilization problem of delayed coupled memristor-based neural networks with pinning control and indefinite derivative approach

An improved fixed-time stabilization problem of delayed coupled memristor-based neural networks with pinning control and indefinite derivative approach

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In this brief, we propose a class of generalized memristor-based neural networks with nonlinear coupling.Based on the set-valued mapping theory, novel Lyapunov indefinite derivative and Memristor theory, the coupled memristor-based neural networks (CMNNs) can achieve fixed-time stabilization (FTS) by designing a proper pinning controller, which randomly controls a small number of neuron nodes.Different from Toast Tongs the traditional Lyapunov method, this paper uses the implementation method of indefinite derivative to deal with the non-autonomous neural network system with nonlinear coupling topology between different neurons.

The system can obtain stabilization in a fixed time and requires fewer conditions.Moreover, the fixed stable setting time estimation of the system is given through a few conditions, which can eliminate Whirlpool WHBS92FLTK W Collection Built In 90cm 3 Speeds A Chimney Cooker Hood the dependence on the initial value.Finally, we give two numerical examples to verify the correctness of our results.