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Bellman's contribution is remembered in the name of the Bellman equation, a central result of dynamic programming which restates an optimization problem in recursive form. Bellman explains the reasoning behind the term dynamic programming in his autobiography, Eye of the Hurricane: An Autobiography: I spent the Fall quarter (of 1950) at RAND ...
This two-level Adaptive Dynamic Programming (ADP) structure resulted in a new generation of Policy Iteration Algorithms for continuous-time systems that significantly improved existing adaptive controllers by allowing them to learn Optimal Control solutions by measuring data online and hence to minimize prescribed performance indices such as ...
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 ...
Adaptive control is the control method used by a controller which must adapt to a controlled system with parameters which vary, or are initially uncertain. [ 1 ] [ 2 ] For example, as an aircraft flies, its mass will slowly decrease as a result of fuel consumption; a control law is needed that adapts itself to such changing conditions.
Adaptive algorithm – Algorithm that changes its behavior at the time it is run; Personalization – Using technology to accommodate the differences between individuals; Adaptive hypermedia – Hypermedia which varies output provided according to a model of the user; Content adaptation – Design approach for distribution to mixed environments
Among the most used adaptive algorithms is the Widrow-Hoff’s least mean squares (LMS), which represents a class of stochastic gradient-descent algorithms used in adaptive filtering and machine learning. In adaptive filtering the LMS is used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean ...
If the parameter space is finite (consisting of finitely many elements), then this robust optimization problem itself is a linear programming problem: for each (,) there is a linear constraint +. If P {\displaystyle P} is not a finite set, then this problem is a linear semi-infinite programming problem, namely a linear programming problem with ...
Markov decision process (MDP), also called a stochastic dynamic program or stochastic control problem, is a model for sequential decision making when outcomes are uncertain. [ 1 ] Originating from operations research in the 1950s, [ 2 ] [ 3 ] MDPs have since gained recognition in a variety of fields, including ecology , economics , healthcare ...