论文标题
熟悉后玛什 - 罗斯神经网络的指数同步
Exponential Synchronization of Memristive HIndmarsh-Rose Neural Networks
论文作者
论文摘要
提出了一个新的神经网络模型，该模型由备忘录和扩散后的后玛斯 - 罗斯方程提出。在状态空间中显示了吸收集的全球耗散动力学。通过尖锐而均匀的分组估计以及通过积分不平等的杠杆作用，可以解决线性网络耦合与纪念性非线性的耦合，当耦合强度满足阈值的阈值时，要在均匀收敛速率下以均匀收敛速率以均匀收敛速率进行指数同步。
A new model of neural networks described by the memristive and diffusive Hindmarsh-Rose equations is proposed. Globally dissipative dynamics is shown with absorbing sets in the state spaces. Through sharp and uniform grouping estimates and by leverage of integral inequalities tackling the linear network coupling against the memristive nonlineariry, it is rigorously proved that exponential synchronization at a uniform convergence rate occurs when the coupling strengths satisfy the threshold conditions quantitatively expressed by the parameters.
