Extracting Interpretable Fuzzy Rules From RBF Networks
Fuzzy Systems and RBF Network
Fuzzy systems and radial-basis-function networks (RBFN) are functionally
equivalent [1]. However, they are different in physical meanings. Fuzzy
systems should satisfy the interpretability conditions whereas RBFN not.
By comparing an interpretable fuzzy system and an RBF network, it has been
found in [2] that the main difference between fuzzy systems and RBFNs is that
fuzzy systems should share a certain number of their membership functions
to reduce the number of fuzzy subsets in a fuzzy partition and therefore to
improve the distinguishability of the fuzzy partition. Based on this
observation, a regularization method has been proposed to extract fuzzy rules
from RBFN [2]. This method can also be used to improve the interpretability
of fuzzy systems that are generated from data.
References
[1] J.-S. R. Jang and C.-T. Sun. Functional equivalence between RBFN and fuzzy
inference systems. IEEE Transactions on Neural Networks, 4:156-158, 1993
[2] Y. Jin, W. von Seelen and B. Sendhoff. Extracting interpretable
fuzzy rules from RBF Neural Networks. Internal Report, IR-INI 2000-02, ISSN
0943-2752,
Institut fuer Neuroinformatik, Ruhr-Universitaet Bochum, Bochum, January 2000
[3] Y. Jin, Fuzzy modeling of high-dimensional systems: complexity reduction
and interpretability improvement. IEEE Transactions on Fuzzy Systems,
8(2):212-221, 2000
For discussions, please contact Yaochu Jin.