Robust model predictive control (robust MPC) enables robots to deal with uncertainties.
Furthermore, based on this black-box LS-SVM model, valid control strategy studies such as predictive control, robust control can be developed.
We propose the new method for calculation of control algorithm parameters for predictive and robust control of linear switched polytopic system.
This is essential to obtain better mechanistic understanding of nano-bio interactions, and to derive quantitatively predictive and robust models for the properties of nanomaterials that have useful domains of applicability.
Subsequently, such hypotheses are scored according to their ability to yield a predictive and robust 3D-QSAR model (Figure 1).
The authors demonstrate that the method is sufficiently predictive and robust to support its use in a drug-screening setting.
Random forest yielded more, and more robust predictive signatures, with nine out of the twelve signatures generated remaining predictive when tested against the CCLE dataset (17-AAG, AZD6244, Erlotinib, Lapatinib, Nultin-3, Paclitaxel, PD0325901, PD0332991, PLX4720).
When we compared CD3/CD28-induced cell death predicted by the PLSR model to experimental measurements, the model showed robust predictive capability (Fig. 4d; R2Y = 0.96, Q2 = 0.68).
We also predicted zero flow duration for the entire river network by developing a robust predictive model relating measured zero flow duration (% months) to environmental predictor variables (based on 43 stream gauges).
The signature with the most robust predictive ability was BRmet50, as it was able to predict prognosis successfully in 23 out of 31 breast cancer datasets (74%) [ 32].
