Furthermore, this paper presents an approach to optimize the robustness of FIOs with respect to disturbances in finite frequency ranges.
Finally, a multi-objective evolutionary algorithm (MOEA) is presented to search reaction rate constants which optimize the robustness of network and compared with the NSGA-II, which is a representation of a class of modern multi-objective evolutionary algorithms.
Novel inverse Gauss-Newton methods were also used to correct for possible motion artifacts and optimize the robustness of detecting optogenetically evoked responses (Fang and Lee, 2013).
The aims were to produce baseline data which would inform decisions for the main trial design (i.e. population, primary outcome, sample size calculation) and to optimize the robustness of trial processes within the study setting.
We target robustness, optimizing the amount of data and the value of the collection period, keeping in mind the goals of autonomous node operation and minimal coordination effort.
This paper compares the two approaches based on pole sensitivity and the complex stability radius measures, respectively, for optimizing the closed-loop stability robustness of digital controllers with respect to finite word length (FWL) errors in fixed-point implementation.
In our study, this is also the equivalent of optimizing the global stability and robustness of the network [16].
In this case, the robustness is achieved by optimizing the system under the worst-case channel condition and it usually leads to a min-max or max-min problem formulation.
By optimizing the gain matrix in fault observer, the robustness of the fault detection is investigated.
