Extending Regularized Least Squares Support Vector Machines for Order Selection of Dynamical Takagi-Sugeno Models-Reference-Cited by-同舟云学术

Extending Regularized Least Squares Support Vector Machines for Order Selection of Dynamical Takagi-Sugeno Models

Published:2020 Issue:2 Volume:53 Page:1182-1187
ISSN:2405-8963
Container-title:IFAC-PapersOnLine
language:en
Short-container-title:IFAC-PapersOnLine

Author:

Kahl Matthias,Kroll Andreas

Publisher

Elsevier BV

Subject

Control and Systems Engineering

Reference16 articles.

1. A review on data-driven linear parameter-varying modeling approaches: A high-purity distillation column case study;Bachnas;Journal of Process Control,2014

2. Order determination and input selection with local model networks;Belz;IFAC-PapersOnLine,2017

3. A survey on switched and piecewise affine system identification;Garulli;IFAC Proceedings Volumes,2012

4. Gringard, M. and Kroll, A. (2017). On optimal offline experiment design for the identification of dynamic TS models: Multi-step signals for uncertainty-minimal consequent parameters. In Proc. 27th Workshop Computational Intelligence, 117–138. Dortmund, Germany.

5. Variable selection algorithm for the construction of MIMO operating point dependent neurofuzzy networks;Hong;IEEE Transactions on Fuzzy Systems,2001

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Federated deep learning algorithm for RPA network security detection;Journal of Cyber Security Technology;2023-11-24

2. Data-Driven Structure Identification of Takagi Sugeno Fuzzy Models Using a Bounded Error Approach;2023 IEEE International Conference on Fuzzy Systems (FUZZ);2023-08-13

3. Application of computer-based support vector machine technology in power customer credit rating;2022 3rd International Conference on Computer Science and Management Technology (ICCSMT);2022-11

4. Identification of a Spatio-Temporal Temperature Model for Laser Metal Deposition;Metals;2021-12-18