Exponential Input‐To‐State Stability of Quaternion‐Valued Memristive Neural Networks: Continuous and Discrete Cases
Ruoxia Li, Jinde Cao, Mahmoud Abdel‐Aty · International Journal of Adaptive Control and Signal Processing · 2024
ABSTRACT This article focuses on the input‐to‐state stability (ISS) issue of quaternion‐valued memristive networks. Employing the quaternion norm tool and the Lyapunov method, two improved conclusions are developed for the continuous networks. After that, via the semidiscretization technique, a new discrete model is designed, and its ISS performance is discussed and subsequently recur to a nonlinear scalarization approach. Less conservative results are obtained since the nonlinear scalarization approach makes the quaternion interval meaningful. Simulations are presented to verify the validity of the outcomes.