Adaptive network based fuzzy classification

Chuen-Tsai Sun*, Jyh Shing Jang

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

5 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. We also employ Kalman filter algorithm to improve the overall performance. 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 discuss the use of Kalman filter algorithm to minimize the square error. 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 veiwpoint of feature selection.

Original languageEnglish
Number of pages4
StatePublished - 1 Dec 1992
EventProceedings of the 1992 Japan - USA Symposium on Flexible Automation Part 2 (of 2) - San Francisco, CA, USA
Duration: 13 Jul 199215 Jul 1992


ConferenceProceedings of the 1992 Japan - USA Symposium on Flexible Automation Part 2 (of 2)
CitySan Francisco, CA, USA

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