TY - JOUR
T1 - Fuzzy logic - a modern perspective
AU - Yen, John
N1 - Funding Information:
We thank Lotfi A. Zadeh, Henri Prade, and Didies Dubois for fruitful technical exchanges regarding fuzzy logic and artificial intelligence. We also thank Reza Langari and Liang Wang for their contributions to our research and teaching related to fuzzy logic. This research was partially supported by National Science Foundation Young Investigator Award No. IRI 9257293.
PY - 1999/1
Y1 - 1999/1
N2 - Traditionally, fuzzy logic (FL) has been viewed in the artificial intelligence (AI) community as an approach for managing uncertainty. In the 1990s, however, fuzzy logic has emerged as a paradigm for approximating a functional mapping. This complementary modern view about the technology offers new insights about the foundation of fuzzy logic, as well as new challenges regarding the identification of fuzzy models. In this paper, we will first review some of the major milestones in the history of developing fuzzy logic technology. After a short summary of major concepts in fuzzy logic, we discuss a modern view about the foundation of two types of fuzzy rules. Finally, we review some of the research in addressing various challenges regarding automated identification of fuzzy rule-based models.
AB - Traditionally, fuzzy logic (FL) has been viewed in the artificial intelligence (AI) community as an approach for managing uncertainty. In the 1990s, however, fuzzy logic has emerged as a paradigm for approximating a functional mapping. This complementary modern view about the technology offers new insights about the foundation of fuzzy logic, as well as new challenges regarding the identification of fuzzy models. In this paper, we will first review some of the major milestones in the history of developing fuzzy logic technology. After a short summary of major concepts in fuzzy logic, we discuss a modern view about the foundation of two types of fuzzy rules. Finally, we review some of the research in addressing various challenges regarding automated identification of fuzzy rule-based models.
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U2 - 10.1109/69.755624
DO - 10.1109/69.755624
M3 - Article
AN - SCOPUS:0032679421
SN - 1041-4347
VL - 11
SP - 153
EP - 165
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
IS - 1
ER -