Exponential State Estimation and Passivity of Fuzzy Quaternion-Valued Memristive Neural Networks: Norm Approach
研究了四元数忆阻神经网络的指数状态估计与无源性，通过引入Takagi-Sugeno模糊规则简化系统，并设计控制器实现估计误差系统的指数稳定，基于Lyapunov理论得到无源性判据。
In this article, we consider exponential estimation and passivity of memristive neural networks with quaternion parameters. A Takagi–Sugeno type rule is introduced into the quaternion memristive neural networks, which makes the system much easier. To achieve the exponential stability of the estimation error system, a proper controller is designed, which derived in the two norm form. Further, the quasi-state estimation condition is also considered. Along with Lyapunov theory, some criteria are obtained to achieve the exponential passivity of the discussed system.