An evolving concept in the identification of an interval fuzzy model of Wiener-Hammerstein nonlinear dynamic systems

DOI10.1016/J.INS.2021.09.004OpenAlexW3197389756MaRDI QIDQ6139443

Igor Škrjanc

Publication date: 19 January 2024

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2021.09.004

zbMATH Keywords

data stream: evolving clusteringfiltered recursive least squaresfuzzy interval model

Mathematics Subject Classification ID

Computer science (68-XX) Systems theory; control (93-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Nonlinear predictive control of dynamic systems represented by Wiener-Hammerstein models
• Fuzzy confidence interval for pH titration curve
• An on-line algorithm for creating self-organizing fuzzy neural networks
• Identification of dynamical systems with a robust interval fuzzy model
• An approach for fuzzy rule-base adaptation using on-line clustering
• Filtering based recursive least squares algorithm for Hammerstein FIR-MA systems
• Data filtering based recursive least squares algorithm for Hammerstein systems using the key-term separation principle
• Recursive Estimation and Time-Series Analysis
• Persistence of excitation conditions and the convergence of adaptive schemes
• Data-driven evolving fuzzy systems using eTS and FLEXFIS: comparative analysis

This page was built for publication: An evolving concept in the identification of an interval fuzzy model of Wiener-Hammerstein nonlinear dynamic systems