This note analyzes the unsupervised fuzzy neural network (FNNU) of Kwan and
Cai and finds the following: the FNNU is a clustering net, not a classifie
r net, and the number of clusters the network settles to may be less or mor
e than the actual number of pattern classes-sometimes it could even be equa
l to the number of training data points! The huge number of connections in
the FNNU can be drastically reduced without degrading its performance, The
algorithm does not have any learning capability for its parameters, Computa
tional experience shows that usually the performance of an multilayer perce
ptron (MLP) is comparable to that of even a supervised version of FNN (trai
ned by gradient descent algorithm) in terms of recognition scores, but an M
LP has a much faster convergence than the supervised version of FNN.