Rule-base self-generation and simplification for data-driven fuzzy models.

DOI10.1016/S0165-0114(03)00160-XzbMath1050.93500OpenAlexW2077806522MaRDI QIDQ1430890

D. A. Linkens, Min-You Chen

Publication date: 27 May 2004

Published in: Fuzzy Sets and Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0165-0114(03)00160-x

zbMATH Keywords

Fuzzy modelingFuzzy clusteringSimilarity analysisRule-base simplification

Mathematics Subject Classification ID

Related Items (13)

On constructing a fuzzy inference framework using crisp decision trees ⋮ Identification and simplification of T-S fuzzy neural networks based on incremental structure learning and similarity analysis ⋮ Symbiotic Evolution to Avoid Linkage Problem ⋮ Validation of methods for ranking fuzzy numbers in decision making ⋮ On-line identification of computationally undemanding evolving fuzzy models ⋮ On fuzzy cluster validity indices ⋮ Fuzzy rule base simplification using multidimensional scaling and constrained optimization ⋮ Interest rate prediction: a neuro-hybrid approach with data preprocessing ⋮ A stability based validity method for fuzzy clustering ⋮ A clustering and SVM regression learning-based spatiotemporal fuzzy logic controller with interpretable structure for spatially distributed systems ⋮ Interpretability issues in data-based learning of fuzzy systems ⋮ Rule generation for hierarchical collaborative fuzzy system ⋮ Evolutionary algorithm-based learning of fuzzy neural networks. II: Recurrent fuzzy neural networks

Cites Work

• Modeling and control of hierarchical systems with fuzzy systems
• Fuzzy identification of systems and its applications to modeling and control
• Cluster Validity with Fuzzy Sets
• Unnamed Item

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