Special Issue: Neural Networks, Fuzzy Systems and Other Computational Intelligence Techniques for Advanced Process Control
Author:
Affiliation:
1. School of Engineering, Merz Court, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
2. Department of Chemical and Biological Engineering, The University of Sheffield, Mappin Street, Sheffield S1 3JD, UK
Abstract
Publisher
MDPI AG
Subject
Process Chemistry and Technology,Chemical Engineering (miscellaneous),Bioengineering
Link
https://www.mdpi.com/2227-9717/11/8/2278/pdf
Reference17 articles.
1. Liu, Y., Xu, S., Hashimoto, S., and Kawaguchi, T. (2020). A reference-model-based neural network control method for multi-input multi-output temperature control system. Processes, 8.
2. Hung, J.C. (2020). DOA estimation in non-uniform noise based on subspace maximum likelihood using MPSO. Processes, 8.
3. Xue, F., Li, X., Zhou, K., Ge, X., Deng, W., Chen, X., and Song, K. (2021). A Quality integrated fuzzy inference system for the reliability estimating of fluorochemical engineering processes. Processes, 9.
4. Zhang, Z., Wei, H., Zhang, W., and Jiang, J. (2021). Ripple attenuation for induction motor finite control set model predictive torque control using novel fuzzy adaptive techniques. Processes, 9.
5. Aguilar-López, R., Mata-Machuca, J.L., and Godinez-Cantillo, V. (2021). A TITO control strategy to increase productivity in uncertain exothermic continuous chemical reactors. Processes, 9.
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