Anytime information processing based on fuzzy and neural network models

Conference Record - IEEE Instrumentation and Measurement Technology Conference

Volumn 2, Issue , 2001, Pages 1247-1252

  Vákonyi Kóczy, Annmária R ^a   Ruano, Antonio ^b   Baranyi, Péter ^a   Takács, Orsolya ^a  

^a BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS   (Hungary)

^b UNIVERSITY OF ALGARVE   (Portugal)

Author keywords

Anytime systems; Complexity reduction; Fuzzy modeling; Neural networks; Singular value decomposition

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; COMPUTATIONAL COMPLEXITY; FUZZY SETS; ITERATIVE METHODS; MEMBERSHIP FUNCTIONS; MULTILAYER NEURAL NETWORKS;

ANYTIME ALGORITHMS; ANYTIME INFORMATION PROCESSING; FUZZY-RULE BASE; SINGULAR VALUE DECOMPOSITION; SOFT COMPUTING METHOD;

DATA PROCESSING;

EID: 0034823109     PISSN: None     EISSN: None     Source Type: Journal    
DOI: 10.1109/IMTC.2001.928275     Document Type: Article

References (16)

1. Identification of nonlinear systems - A survey
2. Anytime algorithms in embedded signal processing systems
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5. Approximation and complexity reduction of the generalized neural network
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8. Practical extension of the SVD based reduction technique for extremely large fuzzy rule bases
9. Approximation and complexity reduction of the generalized neural network
10. Error bound of the SVD based neural network reduction
11. Error-bound for the non-exact SVD-based complexity reduction of the generalized type hybrid neural network with non-singleton consequents
12. Transients in reconfigurable systems