Real-Time Adaptive Machine-Learning-Based Predictive Control of Nonlinear Processes-Reference-Cited by-同舟云学术

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Real-Time Adaptive Machine-Learning-Based Predictive Control of Nonlinear Processes

Published:2019-07-24 Issue:6 Volume:59 Page:2275-2290
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Wu Zhe¹,Rincon David¹,Christofides Panagiotis D.¹²^ORCID

Affiliation:

1. Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, California 90095, United States

2. Department of Electrical and Computer Engineering, University of California, Los Angeles, California 90095, United States

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.9b03055

Reference39 articles.

1. Adaptive Neural Control of Uncertain MIMO Nonlinear Systems

2. A modified particle swarm optimization-based dynamic recurrent neural network for identifying and controlling nonlinear systems

3. A recurrent neural-network-based real-time learning control strategy applying to nonlinear systems with unknown dynamics

4. Issues regarding artificial neural network modeling for reactors and fermenters

5. Applications of artificial neural networks in chemical engineering

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