Fuzzy Hypersphere Neural Network Classifier
نویسندگان
چکیده
In this paper fuzzy hypersphere neural network (FHSNN) is proposed with its learning algorithm, which is used for rotation invariant handwaritten character recognition. The FHSNN utilizes fuzzy sets as pattern classes in which each fuzzy set is an union of fuzzy set hyperspheres. The fuzzy set hypersphere is an ndimensional hypersphere defined by a center point and radius with its membership function. The handwritten characters can be in arbitrary location, scale and orientation. After moment normalization rotation invariant ring-data and Zernike moment features are extracted from characters. Finally, FHSNN algorithm is used to classify these features by its strong ability of discriminating ill-defined character classes. The performance of FHSNN algorithm is compared with other two fuzzy neural network classifiers and found to be superior with respect to the training time, recall time per pattern and the generalization.
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