An approach for fuzzy rule-base adaptation using on-line clustering

DOI10.1016/j.ijar.2003.08.006zbMath1068.68144OpenAlexW2036121527MaRDI QIDQ1881167

Plamen P. Angelov

Publication date: 4 October 2004

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ijar.2003.08.006

Mathematics Subject Classification ID

Fuzzy control/observation systems (93C42) Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items (12)

Evolving fuzzy classifiers using different model architectures ⋮ Fuzzy Relational Control of an Uncertain System ⋮ A novel weight adjustment method for handling concept-drift in data stream classification ⋮ Fault diagnosis with evolving fuzzy classifier based on clustering algorithm and drift detection ⋮ Disturbance rejection using fuzzy model free adaptive control (FMFAC) with adaptive conditional defuzzification threshold ⋮ A rule-based development of incremental models ⋮ An evolving concept in the identification of an interval fuzzy model of Wiener-Hammerstein nonlinear dynamic systems ⋮ On-line identification of computationally undemanding evolving fuzzy models ⋮ TSK-Streams: learning TSK fuzzy systems for regression on data streams ⋮ On-line fuzzy modeling via clustering and support vector machines ⋮ An approach to automatic real-time novelty detection, object identification, and tracking in video streams based on recursive density estimation and evolving Takagi-Sugeno fuzzy systems ⋮ The structural clustering and analysis of metric based on granular space

Uses Software

• DENFIS

Cites Work

• Self-organizing fuzzy control of multi-variable systems using learning vector quantization network
• Evolving rule-based models. A tool for design of flexible adaptive systems.
• Fuzzy identification of systems and its applications to modeling and control
• Construction of quantum states from an optimally truncated von Neumann lattice of coherent states
• Unnamed Item

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