Definition
This entry addresses two topics: learning control and locally weighted regression.
Learning control refers to the process of acquiring a control strategy for a particular control system and a particular task by trial and error. It is usually distinguished from adaptive control (Aström and Wittenmark 1989) in that the learning system is permitted to fail during the process of learning, resembling how humans and animals acquire new movement strategies. In contrast, adaptive control emphasizes single-trial convergence without failure, fulfilling stringent performance constraints, e.g., as needed in life-critical systems like airplanes and industrial robots.
Function approximation results for the function \(y =\sin (2x) + 2\exp (-16x^{2}) + N(0,0.16)\) with (a) a sigmoidal neural...
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Recommended Reading
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Ting, JA., Meier, F., Vijayakumar, S., Schaal, S. (2016). Locally Weighted Regression for Control. In: Sammut, C., Webb, G. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7502-7_493-1
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DOI: https://doi.org/10.1007/978-1-4899-7502-7_493-1
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