Theoretical and Experimental Analysis of a Two-Stage System for Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Neuro-Fuzzy Approach for Compensating Color Backlight Images
Neural Processing Letters
Kernel shapes of fuzzy sets in fuzzy systems for function approximation
Information Sciences: an International Journal
Annealing robust fuzzy basis function for modelling with noise and outliers
International Journal of Computer Applications in Technology
A Fast Fuzzy Neural Modelling Method for Nonlinear Dynamic Systems
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
A qualitative-fuzzy framework for nonlinear black-box system identification
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
A fuzzy approach to image analysis in HLA typing using oligonucleotide microarrays
Fuzzy Sets and Systems
Optimised PWL recursive approximation and its application to neuro-fuzzy systems
Mathematical and Computer Modelling: An International Journal
Learning from biomedical time series through the integration of qualitative models and fuzzy systems
Artificial Intelligence in Medicine
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Fuzzy basis functions (FBF's) which have the capability of combining both numerical data and linguistic information, are compared with other basis functions. Because a FBF network is different from other networks in that it is the only one that can combine numerical and linguistic information, comparisons are made when only numerical data is available. In particular, a FBF network is compared with a radial basis function (RBF) network from the viewpoint of function approximation. Their architectural interrelationships are discussed. Additionally, a RBF network, which is implemented using a regularization technique, is compared with a FBF network from the viewpoint of overcoming ill-posed problems. A FBF network is also compared with Specht's probabilistic neural network and his general regression neural network (GRNN) from an architectural point of view. A FBF network is also compared with a Gaussian sum approximation in which Gaussian functions play a central role. Finally, we summarize the architectural relationships between all the networks discussed in this paper