Title :
Efficient neuro-fuzzy rule generation by parametrized gradient descent for seismic event discrimination
Author :
Gravot, F. ; Muller, J.D. ; Muller, S.
Author_Institution :
Lab. de Detection et de Geophys., CEA, Bruyeres-le-Chatel, France
Abstract :
We show that parametrized gradient descent is very efficient to train fuzzy expert systems with examples. We first present how fuzzy expert systems work and explain their relevance compared to neural classifiers. Then, we describe the proposed learning algorithm. We further explain in more detail its application in each parameter of the fuzzy expert system: the position and the width of the premise fuzzy sets, the rule weights and the conclusion activation levels. Finally, we show the results obtained on real-world problems using several databases and compare them to other classification methods
Keywords :
expert systems; fuzzy neural nets; fuzzy set theory; geophysics computing; gradient methods; knowledge acquisition; learning (artificial intelligence); seismology; fuzzy expert systems; fuzzy neural network; fuzzy set theory; gradient descent method; learning algorithm; pattern classification; rule generation; rule weights; seismic event discrimination; Databases; Electronic mail; Expert systems; Explosions; Fuzzy control; Fuzzy logic; Fuzzy sets; Hybrid intelligent systems; Neural networks; Process control;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.830857
