SVD-based complexity reduction of rule-bases with nonlinear antecedent fuzzy sets

IEEE Transactions on Instrumentation and Measurement

Volumn 51, Issue 2, 2002, Pages 217-221

  Takács, Orsolya ^b   Várkonyi Kóczy, Annamária R ^a,b  

^a IEEE   (Hungary)

^b BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS   (Hungary)

Author keywords

Anytime systems; Complexity reduction; Fuzzy rule bases with nonlinear antecedent fuzzy sets; Nonexact complexity reduction; Singular value decomposition

Indexed keywords

ANYTIME SYSTEMS; SINGLE VALUE DECOMPOSITION (SVD);

ALGORITHMS; COMPUTATIONAL COMPLEXITY; FUZZY SETS; MEMBERSHIP FUNCTIONS; SYSTEMS ANALYSIS;

KNOWLEDGE BASED SYSTEMS;

EID: 0036543621     PISSN: 00189456     EISSN: None     Source Type: Journal    
DOI: 10.1109/19.997815     Document Type: Article

References (7)

1. Fuzzy approximation via grid point sampling and singular value decomposition
2. Reduction of fuzzy rule base via singular value decomposition
3. Singular value-based approximation with non-singleton support
4. Singular value-based approximation with Takagi-Sugeno type fuzzy rule base
5. Complexity reduction of singleton based neuro-fuzzy algorithm
6. Error bound of the SVD based neural networks
7. Anytime information processing based on fuzzy and neural network models