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In addition, the unknown gain matrix is determined by solving a delay-dependent LMI.
The unknown gain matrix is determined by solving a delay-dependent LMI.
The unknown gain matrix is determined by solving a delay-dependent linear matrix inequality.
The observer gain is calculated by solving a delay-dependent matrix differential equation.
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The path between two given targets is obtained by solving a time-dependent optimal control problem.
The feedback is obtained by solving a parameter-dependent linear matrix inequality.
The filter parameters can be readily designed by solving a set of parameter-dependent linear matrix inequalities (LMIs).
This way, the priority is computed as in the Proportional Fair discipline and then multiplied by a delay-dependent factor.
A delay-dependent stability criterion is formulated, and then a sub-optimal guaranteed cost controller is obtained by solving a convex optimization problem in the form of linear matrix inequalities (LMIs).
Based on the Lyapunov Krasovskii functional which contains a triple-integral term, delay-dependent robust state estimation for such T S fuzzy Hopfield neural networks can be achieved by solving a linear matrix inequality (LMI), which can be easily facilitated by using some standard numerical packages.
Next, the stability boundaries of the closed-loop system with multiple time delays are obtained by solving a polynomial eigenvalue problem, and are validated by numerical simulations.
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