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  2. Frank L. Lewis - Wikipedia

    en.wikipedia.org/wiki/Frank_L._Lewis

    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 ...

  3. Robustness (computer science) - Wikipedia

    en.wikipedia.org/wiki/Robustness_(computer_science)

    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 ...

  4. Adaptive algorithm - Wikipedia

    en.wikipedia.org/wiki/Adaptive_algorithm

    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 ...

  5. Reinforcement learning - Wikipedia

    en.wikipedia.org/wiki/Reinforcement_learning

    Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised ...

  6. Adaptive control - Wikipedia

    en.wikipedia.org/wiki/Adaptive_control

    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.

  7. Convolutional neural network - Wikipedia

    en.wikipedia.org/wiki/Convolutional_neural_network

    A convolutional neural network (CNN) is a regularized type of feedforward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [1]

  8. Dynamic Data Driven Applications Systems - Wikipedia

    en.wikipedia.org/wiki/Dynamic_data_driven...

    Dynamic Data Driven Applications Systems ("DDDAS") is a paradigm whereby the computation and instrumentation aspects of an application system are dynamically integrated with a feedback control loop, in the sense that instrumentation data can be dynamically incorporated into the executing model of the application (in targeted parts of the phase-space of the problem to either replace parts of ...

  9. Markov decision process - Wikipedia

    en.wikipedia.org/wiki/Markov_decision_process

    For example, the dynamic programming algorithms described in the next section require an explicit model, and Monte Carlo tree search requires a generative model (or an episodic simulator that can be copied at any state), whereas most reinforcement learning algorithms require only an episodic simulator.