Function Approximation for Learning Control a key sample based approach

B.J. de Kruif

Function Approximation for Learning Control a key sample based approach

B.J. de Kruif

Research output: Thesis › PhD Thesis - Research UT, graduation UT

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Abstract

Two function approximators are introduced in this thesis for use in learning control. These function approximators identify a relation between input and output based on samples. Two different, but closely related function approximators are introduced: the key sample machine and the recursive key sample machine.

Original language	Undefined
Supervisors/Advisors	van Amerongen, Jacob , Supervisor de Vries, Theodorus J.A., Advisor
Award date	18 Jun 2004
Place of Publication	Enschede
Publisher	University of Twente
Print ISBNs	90-365-2050-9
Publication status	Published - 18 Jun 2005

Keywords

METIS-220433
IR-41474

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Final published version, 2.49 MB

Cite this

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author = "{de Kruif}, B.J.",

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N2 - Two function approximators are introduced in this thesis for use in learning control. These function approximators identify a relation between input and output based on samples. Two different, but closely related function approximators are introduced: the key sample machine and the recursive key sample machine.

AB - Two function approximators are introduced in this thesis for use in learning control. These function approximators identify a relation between input and output based on samples. Two different, but closely related function approximators are introduced: the key sample machine and the recursive key sample machine.

KW - METIS-220433

KW - IR-41474

M3 - PhD Thesis - Research UT, graduation UT

SN - 90-365-2050-9

PB - University of Twente

CY - Enschede

ER -