By Shankar Sastry

ISBN-10: 0130043265

ISBN-13: 9780130043269

ISBN-10: 0130043672

ISBN-13: 9780130043672

This quantity surveys the foremost effects and strategies of research within the box of adaptive regulate. concentrating on linear, non-stop time, single-input, single-output structures, the authors supply a transparent, conceptual presentation of adaptive equipment, allowing a severe overview of those concepts and suggesting avenues of extra improvement. 1989 variation

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In contrast to multi-quadratic, thin plate spline, and triangular kernels, the smooth Gaussian activation function satisfies this condition. For alternatives to this widely used activation function, the interested reader is referred to the fourth chapter of Rasmussen’s book on GPR [67]. By altering the norm and its corresponding width parameter σ itself, the geometric shape can be further tuned. The following sections illustrate several geometric shapes based on Gaussian activity and the required parameters.

Finally, all three algorithms are applied to robot control in Part III, where Chapter 7 introduces background information about robot kinematics in general. Next, the algorithms learn to control simulated, anthropomorphic arms of three to seven DoF in Chapter 8. Finally, a more realistic scenario is considered in Chapter 9, where a learning framework for the simulated iCub robot is built. Challenges such as noisy vision and the control of neck joints to follow targets lead to an interesting learning environment.

This approach is termed local learning here, where not only the local models learn but the locality itself may be learned as well, that is, size and shape of the kernels. g. Radial Basis Function Network (RBFN)). The chapter at hand first takes a detailed look at the three features individually. 1. 2 briefly recapitulates constant and linear models. 3. 4. Altogether this chapter only describes the model representation, but does not yet tackle how the locality, that is, the clustering can be learned, which depends on the actual algorithm.

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Adaptive control : stability, convergence, and robustness by Shankar Sastry


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