A flexible neurofuzzy cell structure for general fuzzy inference

 
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1996 (EN)
A flexible neurofuzzy cell structure for general fuzzy inference (EN)

Tzafestas, S (EN)
Raptis, S (EN)
Stamou, G (EN)

N/A (EN)

This paper presents and investigates a neural network structure which can perform general fuzzy inference. This system consists of a number of parallel neural network units which are called ""flexible inference cells"" (FICs). Each FIC implements a single-input/single-output (SISO) IF-THEN rule of a fuzzy knowledge base. The assumption of SISO fuzzy rules allows the implementation of any complex fuzzy inference algorithm (for control or other decision making purposes), since any MIMO (multi-input/multi-output) rule can be decomposed into an equivalent set of MISO (multi-input/single-output) rules, and a MISO rule can be decomposed to a set of SISO rules. The paper discusses the assumptions and requirements for the proposed neurofuzzy structure, and classifies the FICs into four categories. Some results derived by simulation using 3125 exemplar patterns produced computationally are provided. (EN)

journalArticle

Decision Making (EN)
Multi Input Multi Output (EN)
Multi Input Single Output (EN)
Computer simulation (EN)
Knowledge based systems (EN)
Single input single output (EN)
Probability (EN)
Fuzzy Rules (EN)
Single Input Single Output (EN)
Fuzzy Inference (EN)
Fuzzy sets (EN)
Neural Network (EN)
Algorithms (EN)
Multi input multi output (EN)
Decision theory (EN)
Neural networks (EN)
Knowledge Base (EN)
Multi input single output (EN)
Approximation theory (EN)
Inference engines (EN)
Flexible inference cells (EN)

Εθνικό Μετσόβιο Πολυτεχνείο (EL)
National Technical University of Athens (EN)

Mathematics and Computers in Simulation (EN)

1996


ELSEVIER SCIENCE BV (EN)



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