Machine‐learning‐based predictive control of nonlinear processes. Part II: Computational implementation
Author:
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.
1. PanY WangJ. Nonlinear model predictive control using a recurrent neural network. Paper presented at: Proceedings of the IEEE International Joint Conference on Neural Networks; Hong Kong China;2008:2296‐2301.
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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