JOURNAL ARTICLE
Flexible weighted neuro-fuzzy systems
Abstract
In the paper we study new neuro-fuzzy systems. They are called the OR-type fuzzy inference systems (NFIS). Based on the input-output data we learn not only parameters of membership functions but also a type of the systems and aggregating parameters. We propose the weighted T-norm and S-norm to neuro-fuzzy inference systems. Our approach introduces more flexibility to the structure and learning of neuro-fuzzy systems.
Keywords:
Neuro-fuzzy Adaptive neuro fuzzy inference system Computer science Flexibility (engineering) Artificial intelligence Fuzzy control system Fuzzy logic Norm (philosophy) Fuzzy classification Fuzzy inference Machine learning Mathematics Statistics
Metrics
36
Cited By
1.92
FWCI (Field Weighted Citation Impact)
9
Refs
0.87
Citation Normalized Percentile
Is in top 1%
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Citation History
Topics
Fuzzy Logic and Control Systems
Physical Sciences → Computer Science → Artificial Intelligence
Neural Networks and Applications
Physical Sciences → Computer Science → Artificial Intelligence
Fault Detection and Control Systems
Physical Sciences → Engineering → Control and Systems Engineering
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