Dissipativity and exponential state estimation for quaternion-valued memristive neural networks

Abstract

In this paper, the global dissipativity and exponential state estimation for quaternion-valued memristive neural networks are investigated. By starting from basic quaternion properties, several algebraic conditions ensuring the global dissipativity and state estimation problems are derived. It is noteworthy that, based on the vector ordering approach, one can compare the “magnitude” of two different quaternion-valued, and thus the closed convex hull derived by two different quaternion-valued connections can be derived. Simulation results are given to show the validate of the mathematical model and the derived conclusions of the memristive system.