Machine learning: Overview of the recent progresses and implications for the process systems engineering field
| Autor: | Jay H. Lee, Joohyun Shin, Matthew J. Realff |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network Process (engineering) business.industry Computer science General Chemical Engineering Deep learning Big data 02 engineering and technology Machine learning computer.software_genre Data science Field (computer science) Computer Science Applications 020901 industrial engineering & automation 020401 chemical engineering Inductive transfer Reinforcement learning Artificial intelligence 0204 chemical engineering business computer Feature learning |
| Zdroj: | Computers & Chemical Engineering. 114:111-121 |
| ISSN: | 0098-1354 |
| DOI: | 10.1016/j.compchemeng.2017.10.008 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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