Self-tuned Model-Based Predictive Control Using Evolving Fuzzy Model of a Non-linear Dynamic Process-Reference-Cited by-同舟云学术

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Self-tuned Model-Based Predictive Control Using Evolving Fuzzy Model of a Non-linear Dynamic Process

Published:2021-07-28 Issue: Volume: Page:406-421
ISSN:2367-3370
Container-title:Explainable AI and Other Applications of Fuzzy Techniques
language:
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Author:

Stržinar Žiga,Škrjanc Igor

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-82099-2_37

Reference16 articles.

1. Škrjanc, I., Iglesias, J.A., Sanchis, A., Leite, D., Lughofer, E., Gomide, F.: Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: a survey. Inf. Sci. 490, 344–368 (2019)

2. Ding, F., Wang, Y., Ding, J.: Recursive least squares parameter identification algorithms for systems with colored noise using the filtering technique and the auxilary model. Digital Signal Process. 37, 100–108 (2015)

3. Wang, D., Ding, F.: Input-output data filtering based recursive least squares identification for cararma systems. Digital Signal Process. 20(4), 991–999 (2010)

4. Wang, D., Ding, F., Chu, Y.: Data filtering based recursive least squares algorithm for hammerstein systems using the key-term separation principle. Inf. Sci. 222, 203–212 (2013)

5. Ding, F., Duan, H.: Two-stage parameter estimation algorithms for box-jenkins systems. IET Signal Proc. 7(8), 646–654 (2013)

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