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Robustness is the property of being strong and healthy in constitution. When it is transposed into a system, it refers to the ability of tolerating perturbations that might affect the system's functional body.
Robust methods aim to achieve robust performance and/or stability in the presence of bounded modelling errors. The early methods of Bode and others were fairly robust; the state-space methods invented in the 1960s and 1970s were sometimes found to lack robustness, [1] prompting research to improve them. This was the start of the theory of ...
Robustness can encompass many areas of computer science, such as robust programming, robust machine learning, and Robust Security Network. Formal techniques, such as fuzz testing, are essential to showing robustness since this type of testing involves invalid or unexpected inputs. Alternatively, fault injection can be used to test robustness ...
Robust control, an approach to controller design that explicitly deals with uncertainty; Robust decision-making, an iterative decision analytics framework; Robust optimization, a field of mathematical optimization theory; Robust statistics, which maintain their properties even if the underlying distributional assumptions are incorrect
Robustness testing is any quality assurance methodology focused on testing the robustness of software.Robustness testing has also been used to describe the process of verifying the robustness (i.e. correctness) of test cases in a test process.
Robustification as it is defined here is sometimes referred to as parameter design or robust parameter design (RPD) and is often associated with Taguchi methods.Within that context, robustification can include the process of finding the inputs that contribute most to the random variability in the output and controlling them, or tolerance design.
Robust optimization is a field of mathematical optimization theory that deals with optimization problems in which a certain measure of robustness is sought against uncertainty that can be represented as deterministic variability in the value of the parameters of the problem itself and/or its solution.
Robust decision methods seem most appropriate under three conditions: when the uncertainty is deep as opposed to well characterized, when there is a rich set of decision options, and the decision challenge is sufficiently complex that decision-makers need simulation models to trace the potential consequences of their actions over many plausible ...