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The control designs build on quadratically parameter-dependent Lyapunov functions and allow for incorporating information on available bounds on the parameters variation.
The filter design is based on Lyapunov functions with rational dependence on the system parameters and incorporates information on available bounds on the parameters variation.
The feasibility problem allows us to check consistency of entire parameter regions by using upper and lower bounds on the parameters.
A convex optimization approach in terms of linear matrix inequalities, and which incorporates information on available bounds on the parameters variation, is devised to the filter design based on Lyapunov functions with rational dependence on the system parameters.
In addition, the (1 − δ) % two-sided asymptotic confidence bounds on the parameters are easily obtained from the following inequality: widehat{theta}-{z}_{delta /2}.{sqrt{left[{I}_{ii}left widehat{theta}right)right]}}_{i=1,2,cdots, p}
Our bounds on the parameters apply only to demographic variation, not environmental.
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By applying the proposed algorithm to experimental data, a good degree of output prediction was achieved along with bounds on the parameter values.
In this study, the performance of DROT is determined using synthetic data with variable bounds on the parameter set of amplitude, frequency and duration of disturbance.
We first performed several IMa runs, subsequently adjusting the upper bounds on the parameter priors, to determine the most efficient search parameters.
To generate an approximate alternative, we set initial bounds on the parameter space Λ such that N A ∈ {10, 10}, N 0 ∈ {10, 5×10}, and τ ∈ {0, 5×10}.
We obtain the reproductive parameters directly from a field study of the Bimini lemon shark nursery, and we use an exhaustive parameter search to obtain bounds on the other parameters.
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