Title :
A genetic approach to fuzzy learning
Author_Institution :
Istituto di Inf. e Telecommun., Catania Univ., Italy
Abstract :
The approach proposed allows supervised approximation of multi-input/multi-output (MIMO) systems. Typically a small number of fuzzy rules are produced. The learning capacity is considerable, as is shown by the numerous applications developed. The paper gives a significant example of how the fuzzy models developed are generally better than those to be found in recent literature concerning both the approximation capability and simplicity
Keywords :
MIMO systems; encoding; function approximation; fuzzy logic; fuzzy neural nets; genetic algorithms; learning (artificial intelligence); MIMO systems; coding; evolution algorithm; fitness function; function approximation; fuzzy learning; fuzzy logic; fuzzy rules; genetic algorithm; machine learning; supervised approximation; Computational complexity; Fuzzy logic; Genetic algorithms; Interpolation; Machine learning; Performance analysis; Physics; Robots; Supervised learning; Telecommunications;
Conference_Titel :
Neuro-Fuzzy Systems, 1996. AT'96., International Symposium on
Conference_Location :
Lausanne
Print_ISBN :
0-7803-3367-5
DOI :
10.1109/ISNFS.1996.603814
