Abstract
In this paper the implementation of RBF online learning algorithm on the Schneider Electric Quantum programmable automation controllers is proposed. Online recursive mean square algorithm with different modifications is proposed for neural network parameter identification. All matrix operations, functions, algorithms and neural network general structure are programmed in the Structured Text programming language in Unity Pro XL software. The proposed method and software implementation is verified on virtual hydraulic system with parameter identification and level control.
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Körösi, L., Németh, V., Paulusová, J., Kozák, Š. (2014). RBF Neural Network for Identification and Control Using PAC. In: Herrero, Á., et al. International Joint Conference SOCO’13-CISIS’13-ICEUTE’13. Advances in Intelligent Systems and Computing, vol 239. Springer, Cham. https://doi.org/10.1007/978-3-319-01854-6_34
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DOI: https://doi.org/10.1007/978-3-319-01854-6_34
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01853-9
Online ISBN: 978-3-319-01854-6
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