Title :
Learning fuzzy rule-based neural networks for function approximation
Author :
Higgins, C.M. ; Goodman, R.M.
Author_Institution :
Dept. of Electr. Eng., California Inst. of Technol., Pasadena, CA, USA
Abstract :
The authors present a method for the induction of fuzzy logic rules to predict a numerical function from samples of the function and its dependent variables. This method uses an information-theoretic approach based on the authors´ previous work with discrete-valued data (see Proc. Int. Joint. Conf. on Neur. Net., vol.1, p.875-80, 1991). The rules learned can then be used in a neural network to predict the function value based on its dependent variables. An example is shown of learning a control system function
Keywords :
function approximation; fuzzy logic; information theory; learning (artificial intelligence); neural nets; control system function learning; dependent variables; discrete-valued data; function approximation; fuzzy logic rules; information theory; neural network; numerical function prediction; rule induction; Contracts; Control system synthesis; Frequency estimation; Function approximation; Fuzzy logic; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Mathematical model; Neural networks;
Conference_Titel :
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location :
Baltimore, MD
Print_ISBN :
0-7803-0559-0
DOI :
10.1109/IJCNN.1992.287127
