Title :
FuGeNeSys-a fuzzy genetic neural system for fuzzy modeling
Author_Institution :
Inst. of Comput. Sci. & Telecommun., Catania Univ., Italy
fDate :
8/1/1998 12:00:00 AM
Abstract :
The author has developed a novel approach to fuzzy modeling from input-output data. Using the basic techniques of soft computing, the method allows supervised approximation of multi-input multi-output (MIMO) systems. Typically, a small number of rules are produced. The learning capacity of FuGeNeSys 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 literature as concerns simplicity and both approximation and classification capabilities
Keywords :
MIMO systems; approximation theory; fuzzy neural nets; genetic algorithms; learning (artificial intelligence); modelling; FuGeNeSys; I/O data; MIMO systems; fuzzy genetic neural system; fuzzy modeling; input-output data; learning capacity; soft computing; supervised approximation; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Genetic algorithms; Helium; Humans; Learning systems; MIMO; Machine intelligence; Parallel processing;
Journal_Title :
Fuzzy Systems, IEEE Transactions on