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In order to obtain the strong convergence bound of, we give the following two results firstly.
The convergence bound for Volterra series expansion of nonlinear systems is investigated with a novel parametric approach in this study.
In this paper, we consider the strong convergence bound of this Pareto index estimator under second-order regularly varying conditions.
In order to investigate the strong convergence bound of, we require knowing the convergence rate of (2.1).
In this note we study the strong convergence bound of a kind of right censored Pareto index estimator under second-order regularly varying conditions.
This number of iterations is unlikely to be feasible due to the signalling time required, and thus a larger convergence bound ε2 would be used in a real system.
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By examining in detail the basic computational infrastructure provided by the Selective Tuning model and using its functionality, four different binding processes – Convergence Binding and Partial, Full and Iterative Recurrence Binding – are introduced and tied to specific recognition tasks and their time course.
Especially, the designed control depends on the parameters of the exponential convergence rate, the finite-time convergence rate, the bound of the initial states of the master system, and the system parameter.
In addition, we give a convergence time lower bound.
Upper bound convergence of the non-dimensional frequencies to at least four significant figures is demonstrated.
To guarantee convergence, a lower bound is also required in the optimization formulation.
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