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Online identification of evolving Takagi–Sugeno–Kang fuzzy models for crane systems

  • Published:2014-11 Issue: Volume:24 Page:1155-1163
  • ISSN:1568-4946
  • Container-title:Applied Soft Computing
  • language:en
  • Short-container-title:Applied Soft Computing

Author:

Precup Radu-EmilORCID,Filip Horaţiu-Ioan,Rădac Mircea-Bogdan,Petriu Emil M.,Preitl Stefan,Dragoş Claudia-Adina

Funder

Romanian National Authority for Scientific Research ANCS, CNDI–UEFISCDI

NSERC of Canada

Publisher

Elsevier BV

Subject

Software

Reference58 articles.

1. Evolving Rule Based Models: A Tool for Design of Flexible Adaptive Systems;Angelov,2002

2. Incremental learning in fuzzy pattern matching;Sayed Mouchaweh;Fuzzy Sets and Systems,2002

3. Towards incremental fuzzy classifiers;Bouchachia;Soft Computing,2007

4. FLEXFIS. A robust incremental learning approach for evolving Takagi-Sugeno fuzzy models;Lughofer;IEEE Transactions on Fuzzy Systems,2008

5. Evolving Fuzzy Systems – Methodologies, Advanced Concepts and Applications;Lughofer,2011

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1. Data-Driven Identification of Crane Dynamics Using Regularized Genetic Programming;Applied Sciences;2024-04-20

2. Predicting the State of Crane Spreader for Steel Bar Delivery Based on Machine Learning;2023 IEEE 13th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER);2023-07-11

3. Autonomous learning for fuzzy systems: a review;Artificial Intelligence Review;2022-12-15

4. Observer Desing for Non Linear Takagi-Sugeno Fuzzy Systems. Application to Fault Tolerant Control;Studies in Systems, Decision and Control;2022

5. Anti-swing radial basis neuro-fuzzy linear quadratic regulator control of double link rotary pendulum;Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering;2021-09-15
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