Topic
Fuzzy associative matrix
About: Fuzzy associative matrix is a research topic. Over the lifetime, 8027 publications have been published within this topic receiving 194790 citations.
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TL;DR: The principles of threshold fuzzification and fuzzy rule-based threshold adaption are outlined, and the usefulness of fuzzy logic for man-machine interaction is pointed out.
57 citations
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TL;DR: The technique overcomes the difficulties in a conventional syntactic approach to handwritten character recognition, including problems of choosing a starting or reference point, scaling, and learning by machines, and out-performs the straight forward ID3 approach.
Abstract: Presents a technique to produce fuzzy rules based on the ID3 approach and to optimize defuzzification parameters by using a two-layer perceptron. The technique overcomes the difficulties in a conventional syntactic approach to handwritten character recognition, including problems of choosing a starting or reference point, scaling, and learning by machines. The authors' technique provides: a way to produce meaningful and simple fuzzy rules; a method to fuzzify ID3-derived rules to deal with uncertain, noisy, or fuzzy data; and a framework to incorporate fuzzy rules learned from the training data and those extracted from human recognition experience. The authors' experimental results on NIST Special Database 3 show that the technique out-performs the straight forward ID3 approach. Moreover, ID3-derived fuzzy rules can be combined with an optimized nearest neighbor classifier, which uses intensity features only, to achieve a better classification performance than either of the classifiers. The combined classifier achieves a correct classification rate of 98.6% on the test set.
57 citations
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TL;DR: Different characterizations of semi-continuous and semi-closed mappings between fuzzy topological spaces are studied and fuzzy semi-connectedness is introduced and studied to some extent.
57 citations
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TL;DR: This study discusses each of the algorithms in great detail and offers a thorough comparative analysis and compares the performances of these algorithms in a medical diagnosis classification problem, namely Aachen Aphasia Test.
57 citations