Autosoft Journal

Online Manuscript Access


Deep learning control for complex and large scale cloud systems


Authors



Abstract

Deep learning attempts to model high level perceptions in data using deep graph representations and creating models to learn these representations from large-scale unlabeled signals. Efficient unsupervised feature learning is extracted by deep learning algorithms and with multiple processing layers, composed of multiple linear and non-linear transformations. Actual systems become more and more complex with huge numbers of state variables and control of such large and complex systems with chaotic behavior, which needs more information about systems. Deep learning control by discovering continoiusly almost all possible information seems to be a reasonable approach to model and control largescale and complex systems. Recent advancements in machine learning algorithms and platforms are leading to deep learning controllers in real-time applications. The goal of this paper is to describe the concept of deep learning control and explain how cloud fog computing and edge analytics could handle massive amount of real time data streams from Cyber Physical Systems (CPS).


Keywords


Pages

Total Pages: 3
Pages: 389-391

DOI
10.1080/10798587.2017.1329245


Manuscript ViewPdf Subscription required to access this document

Obtain access this manuscript in one of the following ways


Already subscribed?

Need information on obtaining a subscription? Personal and institutional subscriptions are available.

Already an author? Have access via email address?


Published

Volume: 23
Issue: 3
Year: 2017

Cite this document


References

JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present




CONTACT INFORMATION


TSI Press
18015 Bullis Hill
San Antonio, TX 78258 USA
PH: 210 479 1022
FAX: 210 479 1048
EMAIL: tsiepress@gmail.com
WEB: http://www.wacong.org/tsi/