Machine‐learning‐based predictive control of nonlinear processes. Part II: Computational implementation-Reference-Cited by-同舟云学术

Machine‐learning‐based predictive control of nonlinear processes. Part II: Computational implementation

Published:2019-07-31 Issue:11 Volume:65 Page:
ISSN:0001-1541
Container-title:AIChE Journal
language:en
Short-container-title:AIChE J

Author:

Wu Zhe¹,Tran Anh¹,Rincon David¹,Christofides Panagiotis D.¹²^ORCID

Affiliation:

1. Department of Chemical and Biomolecular Engineering University of California Los Angeles California

2. Department of Electrical and Computer Engineering University of California Los Angeles California

Funder

National Science Foundation

Department of Energy

Publisher

Wiley

Subject

General Chemical Engineering,Environmental Engineering,Biotechnology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/aic.16734

Reference21 articles.

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2. Model Predictive Control of Unknown Nonlinear Dynamical Systems Based on Recurrent Neural Networks

3. Recurrent neural network for solving model predictive control problem in application of four-tank benchmark

4. PethickM LiddleM WersteinP HuangZ. Parallelization of a backpropagation neural network on a cluster computer. Paper presented at: Proceedings of the International Conference on Parallel and Distributed Computing and Systems; Marina del Rey California;2003:574‐582.

5. A framework for parallel and distributed training of neural networks

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