A neural network architecture for classification of fuzzy inputs

Abstract

A neural network for classification problems with fuzzy inputs is proposed. A fuzzy input is represented as an LR-type fuzzy set. A generalized pocket algorithm, called fuzzy pocket algorithm, that uses LR-type fuzzy sets operations and defuzzification method is proposed to train a linear threshold unit (LTU). This LTU node will classify as many fuzzy input instances as possible. Afterward, FV nodes that represent fuzzy vectors will then be generated and expanded, by proposed FVGE learning algorithm, to classify those fuzzy input instances that cannot be classified by the LTU node. The similarity degree between FV nodes and fuzzy inputs is measured by the fuzzy subsethood degree. The network structure is automatically generated. The number of hidden nodes generated depends on the overlapping degree of training instances. Besides, on-line learning is supplied, and parameters used are few and insensitive. The relationship between proposed model and hyperbox-based classifiers, e.g., Fuzzy ART series and Fuzzy Min-Max series, is also discussed. Two sample problems, heart disease and knowledge-based evaluator, are considered to illustrate the working of the proposed model. The experimental results are very encouraging.