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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 7/2019

20.06.2018 | Original Article

Deep Boltzmann machine for nonlinear system modelling

verfasst von: Wen Yu, Erick de la Rosa

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 7/2019

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Abstract

Deep Boltzmann machine (DBM) has been successfully applied in classification, regression and time series modeling. For nonlinear system modelling, DBM should also have many advantages over the other neural networks, such as input features extraction and noise tolerance. In this paper, we use DBM to model nonlinear systems by calculating the probability distributions of the input and output. Two novel weight updating algorithms are proposed to obtain these distributions. We use binary encoding and conditional probability transformation methods. The proposed methods are validated with two benchmark nonlinear systems.
Vorheriger Artikel Predicting algal appearance on mortar surface with ensembles of adaptive neuro fuzzy models: a comparative study of ensemble strategies
Nächster Artikel Optimal pricing decision for supply chains with risk sensitivity and human estimation

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Metadaten
Titel
Deep Boltzmann machine for nonlinear system modelling
verfasst von
Wen Yu
Erick de la Rosa
Publikationsdatum
20.06.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 7/2019
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-018-0847-0

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