In particular, foreshadowing our Step 1 results, we will find that most methods (except LR) in this study that produce P-values in fact produce conservative ones, with the degree of conservativeness method-dependent.
The finite volume constraint on the cell-average guarantees the numerical conservativeness of the method.
A numerical example is given to show the effectiveness and less conservativeness of the method.
These literatures all use the Lyapunov-Krasovskii functionals (LKFs) method, conservativeness comes from two things: the choice of functional and the bound on its derivative.
The main contribution of this study lies in that a new parameterized linear matrix inequality (LMI) technique is proposed to reduce the conservativeness of the method, and an LMI-based fuzzy suboptimal sampled-data control design is developed for the nonlinear coupled ODE PDE system based on this parameterized LMI technique.
This is likely due to the fact that little variation was found in those genes, the presence of identical sequences that reduced the data set even more, and the inherent conservativeness of the methods.
Numerical examples are presented to illustrate the feasibility and less conservativeness of the proposed method.
Some numerical examples are given to show the effectiveness and less conservativeness of the proposed methods.
Finally, numerical examples are provided to illustrate the less conservativeness of the proposed methods than some existing results.
For nonlinear systems, the applicability of the developed filtering result is confirmed by a longitudinal flight system, and an additional example for linear system is presented to demonstrate the less conservativeness of the proposed design method.
The over-conservativeness of the current AISC/LRFD method comes mainly from neglecting the contribution of the concrete in flexure and the different behavior of composite beam columns from pure steel beam columns.
