Deep learning control for complex and large scale cloud systems. Issue 3 (3rd July 2017)

• Record Type: Journal Article
• Authors: Roopaei, Mehdi; Rad, Paul; Jamshidi, Mo
• Is Part Of: Intelligent automation & soft computing. Volume 23:Issue 3(2017)
• Page Start: 389
• Page End: 391
• Publication Date: 2017-07-03
• Subjects: Deep Learning Control -- Internet of Everything (IOT) -- Dynamic Systems and Control -- Large Scale Systems -- Cloud Computing -- Cyber Physical Systems; Artificial intelligence -- Periodicals; Intelligent control systems -- Periodicals; 003.5
• DOI: 10.1080/10798587.2017.1329245
• Languages: English
• ISSNs: 1079-8587

Abstract

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).