Self-evolving neural networks for rule-based data processing

Abstract

Two training algorithms for self-evolving neural networks are discussed for rule-based data analysis. Efficient classification is achieved with a fewer number of automatically added clusters, and application data is analyzed by interpreting the trained neural network as a fuzzy rule-based system. The learning vector quantization algorithm has been modifified, acquiring the self-evolvement character in the prototype neuron layer based on sub-Bayesian decision making. The number of required prototypes representing fuzzy rules is automatically determined by the application data set. This method, compared with others, shows better classification results for data sets with high noise or overlappi..