Exponential State Estimation for Stochastically Disturbed Discrete-Time Memristive Neural Networks: Multiobjective...

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Materials Science

Background:

• Memristive devices are crucial for advanced electronics.
• Accurate state estimation is vital for memristor-based system control.
• Existing methods may not fully address stochastic dynamics.

Purpose of the Study:

• To develop a robust state estimation method for discrete-time memristive models.
• To ensure the exponential mean-square stability of the state estimation error.
• To derive control gain using linear matrix inequality (LMI) and optimize performance.

Main Methods:

• Stochastic analysis techniques applied to discrete-time memristive models.
• Development of sufficient conditions for exponential mean-square stability.
• Utilization of linear matrix inequality (LMI) for control gain calculation.
• Extension to multiobjective optimization for performance bounds.

Main Results:

• Sufficient formulas derived for ensuring exponential mean-square stability of the error model.
• Control gain matrix calculable via LMI.
• Maximum bound of the active function and minimum bound of disturbance attenuation derived.
• Simulation results validate the proposed methodology.

Conclusions:

• The proposed stochastic analysis provides a reliable framework for memristive model state estimation.
• The LMI-based approach ensures system stability and allows for performance optimization.
• This work contributes to the precise control of memristor-based systems.