具有变时滞的模糊Cohen-Grossberg型神经网络在有限时间内的控制同步

Finite Time Control Synchronization of Fuzzy Cohen-Grossberg Type Neural Networks with Time-varying Delays

【目的】研究一类具有变时滞的模糊Cohen-Grossberg型神经网络在有限时间内的同步。【方法】使用Lyapunov稳定性理论和一些不等式方法,并恰当控制外部输入条件。【结果】得到新的模糊Cohen-Grossberg型神经网络在有限时间内同步的充分条件,且驱动系统和响应系统在有限时间内实现同步。【结论】之前的一些关于神经网络的工作,驱动系统和响应系统是在当时间t→+∞实现同步,相比之下本文结论更加高效实用。

[Purposes]The finite-time synchronization for a class of fuzzy Cohen-Grossberg neural networks with time-varying delays is studied. [Methods]Through Lyapunov stability theory and some inequality methods, and under the proper control of external input conditions. [Findings]Some new sufficient conditions for the fuzzy Cohen-Grossberg neural network in finite time synchronization are obtained, and the synchronization between the drive system and the response system is realized in a finite time.[Conclusions]In the past some neural network articles, the drive system and the response system are synchronized in the time t →＋∞. In contrast, the conclusions of this paper are more efficient and practical.