Machine learning: Overview of the recent progresses and implications for the process systems engineering field

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Machine learning (ML) has recently gained in popularity, spurred by well-publicized advances like deep learning and widespread commercial interest in big data analytics. Despite the enthusiasm, some renowned experts of the field have expressed skepticism, which is justifiable given the disappointment with the previous wave of neural networks and other AI techniques. On the other hand, new fundamental advances like the ability to train neural networks with a large number of layers for hierarchical feature learning may present significant new technological and commercial opportunities. This paper critically examines the main advances in deep learning. In addition, connections with another ML branch of reinforcement learning are elucidated and its role in control and decision problems is discussed. Implications of these advances for the fields of process and energy systems engineering are also discussed. (C) 2017 Elsevier Ltd. All rights reserved.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
2018-06
Language
English
Article Type
Article
Citation

COMPUTERS & CHEMICAL ENGINEERING, v.114, pp.111 - 121

ISSN
0098-1354
DOI
10.1016/j.compchemeng.2011.10.008
URI
http://hdl.handle.net/10203/244990
Appears in Collection
CBE-Journal Papers(저널논문)
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