Journal of Software Engineering and Applications

Volume 17, Issue 8 (August 2024)

ISSN Print: 1945-3116   ISSN Online: 1945-3124

Google-based Impact Factor: 2  Citations  

A Neuro T-Norm Fuzzy Logic Based System

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DOI: 10.4236/jsea.2024.178035    47 Downloads   227 Views  
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ABSTRACT

In this study, we are first examining well-known approach to improve fuzzy reasoning model (FRM) by use of the genetic-based learning mechanism [1]. Later we propose our alternative way to build FRM, which has significant precision advantages and does not require any adjustment/learning. We put together neuro-fuzzy system (NFS) to connect the set of exemplar input feature vectors (FV) with associated output label (target), both represented by their membership functions (MF). Next unknown FV would be classified by getting upper value of current output MF. After that the fuzzy truths for all MF upper values are maximized and the label of the winner is considered as the class of the input FV. We use the knowledge in the exemplar-label pairs directly with no training. It sets up automatically and then classifies all input FV from the same population as the exemplar FVs. We show that our approach statistically is almost twice as accurate, as well-known genetic-based learning mechanism FRM.

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Tserkovny, A. (2024) A Neuro T-Norm Fuzzy Logic Based System. Journal of Software Engineering and Applications, 17, 638-663. doi: 10.4236/jsea.2024.178035.

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