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Need for Data Processing Naturally Leads to Fuzzy Logic (and Neural Networks): Fuzzy Beyond Experts and Beyond Probabilities
Author(s) -
Kreinovich Vladik,
Nguyen Hung T.,
Sriboonchitta Songsak
Publication year - 2016
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21785
Subject(s) - fuzzy logic , neuro fuzzy , artificial intelligence , fuzzy set operations , computer science , type 2 fuzzy sets and systems , defuzzification , fuzzy number , fuzzy classification , meaning (existential) , data mining , machine learning , fuzzy control system , mathematics , fuzzy set , epistemology , philosophy
Fuzzy techniques have been originally designed to describe imprecise (“fuzzy”) expert knowledge. Somewhat surprisingly, fuzzy techniques have also been successfully used in situations without expert knowledge, when all we have is data. In this paper, we explain this surprising phenomenon by showing that the need for optimal processing of data (including crisp data) naturally leads to fuzzy and neural data processing techniques. This result shows the potential of fuzzy data processing. To maximally utilize this potential, we need to provide an operational meaning of the corresponding fuzzy degrees. We show that such a meaning can be extracted from the above justification of fuzzy techniques. It turns out that, in contrast to probabilistic uncertainty, the natural operational meaning of fuzzy degrees is indirect—similarly to the operational meaning of geometry and physics in general relativity.