A Neuro-fuzzy classifier and its applications

Chuen-Tsai Sun*, Jyh Shing Jang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

99 Scopus citations


Fuzzy classification is the task of partitioning a feature space into fuzzy classes. A learn-by-example mechanism is desirable to automate the construction process of a fuzzy classifier. In this paper we introduce a method of employing adaptive networks to solve a fuzzy classification problem. System parameters, such as the membership functions defined for each feature and the parameterized t-norms used to combine conjunctive conditions are calibrated with backpropagation. To explain this new approach, first we introduce the concept of adaptive networks and derive a supervised learning procedure based on a gradient descent algorithm to update the parameters in an adaptive network. Next we apply the proposed architecture to two problems: two-spiral classification and Iris categorization. From the experiment results, it is summarized that the adaptively adjusted classifier performs well on an Iris classification problem. The results are discussed from the viewpoint of feature selection.

Original languageEnglish
Title of host publication1993 IEEE International Conference on Fuzzy Systems
PublisherPubl by IEEE
Number of pages5
ISBN (Print)0780306155
StatePublished - Mar 1993
EventSecond IEEE International Conference on Fuzzy Systems - San Francisco, CA, USA
Duration: 28 Mar 19931 Apr 1993

Publication series

Name1993 IEEE International Conference on Fuzzy Systems


ConferenceSecond IEEE International Conference on Fuzzy Systems
CitySan Francisco, CA, USA

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