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New Algebraic Criteria for Global Exponential Periodicity and Stability of Memristive Neural Networks with Variable Delays

机译:具有变时滞忆阻神经网络的全局指数周期和稳定性的新代数准则

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This paper concentrates on the problem of global exponential periodicity and stability of memristive neural networks with variable delays. By constructing the appropriate Lyapunov functionals and utilizing some inequality techniques, new algebraic criteria are proposed to guarantee the existence and global exponential stability of periodic solution of the considered system. In addition, the proposed theoretical results not only expand and complement the earlier publications, but also are easy to be checked with the parameters of system itself. A numerical example is given to demonstrate the effectiveness of our results.
机译:本文着重研究具有可变时滞的忆阻神经网络的全局指数周期和稳定性问题。通过构造适当的Lyapunov泛函并利用一些不等式技术,提出了新的代数准则,以保证所考虑系统周期解的存在性和全局指数稳定性。另外,所提出的理论结果不仅扩展和补充了较早的出版物,而且易于通过系统本身的参数进行检查。数值例子说明了我们的结果的有效性。

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