Deep learning control for complex and large scale cloud systems
Mehdi Roopaei, Paul Rad, Mo M. Jamshidi · Intelligent Automation & Soft Computing · 2017
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).