Two training algorithms for self-evolving neural networks are discusse
d for rule-based data analysis, Efficient classification is achieved w
ith a fewer number of automatically added clusters, and application da
ta is analyzed by interpreting the trained neural network as a fuzzy r
ule-based system. The learning vector quantization algorithm has been
modified, acquiring the self-evolvement character in the prototype neu
ron layer based on sub-Bayesian decision making, The number of require
d prototypes representing fuzzy rules is automatically determined by t
he application data set, This method, compared with others, shows bett
er classification results for data sets with high noise or overlapping
classification boundaries. The classifying radial basis function netw
orks are generalized into multiple shape basis function networks, The
learning algorithm discussed is capable of adding nea neurons represen
ting self-evolving clusters of different shapes and sizes dynamically,
This shows a clear reduction in number of neurons or the number of fu
zzy rules generated, and the classification accuracy is increased sign
ificantly, This improvement is highly relevant in developing neural ne
tworks that are functionally equivalent to fuzzy classifiers since the
transparency is strongly related to the compactness of the system.