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Robustification is a form of optimisation whereby a system is made less sensitive to the effects of random variability, or noise, that is present in that
Robustification
Parameter controlling the machine learning process
mission critical control systems without significant simplification and robustification. In turn, hyperparameter choices can affect not only predictive performance
Hyperparameter (machine learning)
Hyperparameter_(machine_learning)
resulting 'loop-shape' is then 'robustified' through optimization. Robustification usually has little effect at high and low frequencies, but the response
H-infinity_loop-shaping
Statistical methods to improve the quality of manufactured goods
manufacture, environment and cumulative damage. This is sometimes called robustification. Robust parameter designs consider controllable and uncontrollable
Taguchi_methods
Lebanese-American author (born 1960)
2010). Taleb has described his main challenge as mapping his ideas of "robustification" and "antifragility", that is, how to live and act in a world we do
Nassim_Nicholas_Taleb
Business management method
X, design of experiments (DOE), Taguchi methods, tolerance design, robustification and Response Surface Methodology for a single or multiple response
Design_for_Six_Sigma
Study of uncertainty in the output of a mathematical model or system
Perturbation analysis Probabilistic design Probability bounds analysis Robustification ROC curve Uncertainty quantification Variance-based sensitivity analysis
Sensitivity_analysis
Discipline within engineering design
variables and parameters. This second approach is sometimes referred to as robustification, parameter design or design for six sigma. Though the laws of physics
Probabilistic_design
interaction between control and uncontrollable noise variables by robustification—finding the settings of the control factors that minimize response
Robust_parameter_design
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