scholarly journals Extracting symbolic knowledge from recurrent neural networks—A fuzzy logic approach

2009 ◽  
Vol 160 (2) ◽  
pp. 145-161 ◽  
Author(s):  
Eyal Kolman ◽  
Michael Margaliot
1998 ◽  
Vol 120 (1) ◽  
pp. 95-101 ◽  
Author(s):  
O. K. Rediniotis ◽  
G. Chrysanthakopoulos

The theory and techniques of Artificial Neural Networks (ANN) and Fuzzy Logic Systems (FLS) are applied toward the formulation of accurate and wide-range calibration methods for such flow-diagnostics instruments as multi-hole probes. Besides introducing new calibration techniques, part of the work’s objective is to: (a) apply fuzzy-logic methods to identify systems whose behavior is described in a “crisp” rather than a “linguistic” framework and (b) compare the two approaches, i.e., neural network versus fuzzy logic approach, and their potential as universal approximators. For the ANN approach, several network configurations were tried. A Multi-Layer Perceptron with a 2-node input layer, a 4-node output layer and a 7-node hidden/middle layer, performed the best. For the FLS approach, a system with center average defuzzifier, product-inference rule, singleton fuzzifier, and Gaussian membership functions was employed. The Fuzzy Logic System seemed to outperform the Neural Network/Multi-Layer Perceptron.


1998 ◽  
Author(s):  
Thomas Meitzler ◽  
Regina Kistner ◽  
Bill Pibil ◽  
Euijung Sohn ◽  
Darryl Bryk ◽  
...  

Author(s):  
Abdoul Azize Kindo ◽  
Guidedi Kaladzavi ◽  
Sadouanouan Malo ◽  
Gaoussou Camara ◽  
Theodore Marie Yves Tapsoba ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document