Title :
A new method for generating fuzzy classification systems using RBF neurons with extended RCE learning
Author :
Halgamuge, S.K. ; Poechmueller, W. ; Pfeffermann, A. ; Schweikert, P. ; Glesner, M.
Author_Institution :
Inst. of Microelectron. Syst., Darmstadt Univ. of Technol, Germany
fDate :
27 Jun-2 Jul 1994
Abstract :
A new method is presented combining the advantages of fuzzy inference and neural network learning. A three-layer radial basis function (RBF) network is used to extract rules and to identify the necessary membership functions of the inputs for a fuzzy classification system. The results obtained applying this new method to IRIS-classification are similar to that of other fuzzy-neural approaches, but only lesser number of rules and membership functions are necessary. This system based on RBF-neurons and extended restricted coulomb energy (RCE) learning allows very fast construction of expert knowledge only from input/output data without externally provided expert help and superfluous input features can be removed automatically after training the network
Keywords :
feedforward neural nets; fuzzy neural nets; fuzzy systems; inference mechanisms; learning (artificial intelligence); RBF neurons; fuzzy classification systems; fuzzy inference; membership functions; radial basis function network; restricted coulomb energy learning; Euclidean distance; Feeds; Fuzzy neural networks; Fuzzy systems; Heuristic algorithms; Iris; Microelectronics; Neural networks; Neurons; Radial basis function networks;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374393
