NDLI: An approach to exploit non-optimized data for efficient control of unknown systems through neural and kernel models

Content Provider	IEEE Xplore Digital Library
Author	Cervellera, C. Gaggero, M. Maccio, D. Marcialis, R.
Copyright Year	2014
Description	Author affiliation: Inst. of Intell. Syst. for Autom., Genoa, Italy (Cervellera, C.; Gaggero, M.; Maccio, D.; Marcialis, R.)
Abstract	In this paper, efficient real time control strategies are devised for systems with unknown state equation, based only on a set of data inherited from non-optimized, possibly inefficient, operation of the system, in the case in which experimenting online with the latter is impossible or costly. Neural networks and kernel smoothing models are employed as architectures for learning the system dynamics. The former require an offline training phase to learn the state equation, whereas the latter exploit the available data in a direct fashion, thus making the proposed approach directly applicable online and able to exploit new available data without the need of an offline training. Convergence properties of the proposed algorithm for generating the control strategies are provided under suitable hypotheses. Simulation results on classic benchmark systems are reported for performance evaluation, also through a comparison with the SARSA reinforcement learning algorithm.
Starting Page	1856
Ending Page	1863
File Size	559028
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889794
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mathematical model Approximation methods Kernel Equations Trajectory Vectors Data models
Content Type	Text
Resource Type	Article

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