MATEC Web Conf.
Volume 292, 201923rd International Conference on Circuits, Systems, Communications and Computers (CSCC 2019)
|Number of page(s)||6|
|Published online||24 September 2019|
The use of fuzzy logic in classification
1Department of Electrical & Computer Engineering Florida International University. Miami, USA
When determining the degree of coincidence of any multi-feature obtained information, received in the form of a fuzzy vector, to a pre-established known pattern, two general steps should be followed. The first step is to eliminate the features that have little or no effect to the final results and to maintain only those that will influence the pattern recognition. This step could be defined as the classification process and is imperative for the simplification of the problem. One example of classification that could considerably reduce system costs is when using sensors distributed along an industrial process to manage information at a central location. Several methods could be used for classification, such as statistical methods, rough sets, fuzzy logic or information theory. The second step is to find out the correlation between the received fuzzy vector and the vector defining the known pattern using the previously selected features. For this part, the use of fuzzy logic is extremely convenient. The present work analyzes some of the methods used for classification and pattern recognition based on concrete and practical examples
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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