Title :
Full reinforcement operators in aggregation techniques
Author :
Yager, Ronald R. ; Rybalov, Alexander
Author_Institution :
Machine Intelligence Inst., Iona Coll., New Rochelle, NY, USA
fDate :
12/1/1998 12:00:00 AM
Abstract :
We introduce the concept of upward reinforcement in aggregation as one in which a collection of high scores can reinforce or corroborate each other to give an even higher score than any of the individual arguments. The concept of downward reinforcement is also introduced as one in which low scores reinforce each other. Our concern is with full reinforcement aggregation operators, those exhibiting both upward and downward reinforcement. It is shown that the t-norm and t-conorm operators are not full reinforcement operators. A class of operators called fixed identity MICA operators are shown to exhibit the property of full reinforcement. We present some families of these operators. We use the fuzzy system modeling technique to provide further examples of these operators
Keywords :
fuzzy logic; knowledge representation; learning (artificial intelligence); aggregation techniques; fixed identity MICA operators; full reinforcement operators; fuzzy system modeling; t-conorm operators; t-norm; upward reinforcement; Commutation; Diagnostic expert systems; Diseases; Fuzzy sets; Fuzzy systems; Humans; Information retrieval; Intelligent systems; Logic; Pattern recognition;
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
DOI :
10.1109/3477.735386