Title :
Are artificial neural networks white boxes?
Author :
Kolman, Eyal ; Margaliot, Michael
Author_Institution :
Sch. of Electr. Eng.-Syst., Tel Aviv Univ., Israel
fDate :
7/1/2005 12:00:00 AM
Abstract :
In this paper, we introduce a novel Mamdani-type fuzzy model, referred to as the all-permutations fuzzy rule base (APFRB), and show that it is mathematically equivalent to a standard feedforward neural network. We describe several applications of this equivalence between a neural network and our fuzzy rule base (FRB), including knowledge extraction from and knowledge insertion into neural networks.
Keywords :
artificial intelligence; feedforward neural nets; fuzzy logic; knowledge acquisition; Mamdani type fuzzy model; all permutation fuzzy rule base; artificial neural networks; feedforward neural network; knowledge extraction; knowledge insertion; Artificial neural networks; Computer networks; Equations; Fuzzy neural networks; Fuzzy reasoning; Fuzzy sets; Fuzzy systems; Logistics; Mathematical model; Neural networks; Feedforward neural networks; hybrid intelligent systems; knowledge-based networks; rule extraction; rule generation; rule refinement; Algorithms; Computer Simulation; Computing Methodologies; Decision Support Techniques; Fuzzy Logic; Models, Biological; Models, Statistical; Neural Networks (Computer); Numerical Analysis, Computer-Assisted; Pattern Recognition, Automated; Stochastic Processes;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2005.849843
