Exponential Input‐To‐State Stability of Quaternion‐Valued Memristive Neural Networks: Continuous and Discrete Cases
Copyright policy )Exponential Input‐To‐State Stability of Quaternion‐Valued Memristive Neural Networks: Continuous and Discrete Cases
ABSTRACTThis 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.
- Southeast University China (People's Republic of)
- Sohag University Egypt
- Shaanxi Normal University China (People's Republic of)
- Ahlia University Bahrain
input-to-state stability, norm, quaternion continuous and discrete, Networked control, Quaternion and other division algebras: arithmetic, zeta functions, memristive system, Input-output approaches in control theory, Exponential stability
input-to-state stability, norm, quaternion continuous and discrete, Networked control, Quaternion and other division algebras: arithmetic, zeta functions, memristive system, Input-output approaches in control theory, Exponential stability
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