Neuro-Fuzzy refers to combinations of artificial neural networks and fuzzy logic. Neuro-fuzzy was proposed by J. S. R. Jang. Neuro-fuzzy hybridization results in a hybrid intelligent system that synergizes these two techniques by combining the human-like reasoning style of fuzzy systems with the learning and connectionist structure of neural networks. Neuro-fuzzy system (the more popular term is used henceforth) incorporates the human-like reasoning style of fuzzy systems through the use of fuzzy sets and a linguistic model consisting of a set of IF-THEN fuzzy rules. The main strength of neuro-fuzzy systems is that they are universal approximators with the ability to solicit interpretable IF-THEN rules.
Definition of Fuzzy Sets used to represent linguistic value used in the fuzzy
Although generally assumed to be the realization of a fuzzy system through connectionist networks, this term is also used to describe some other configurations including:
· Deriving fuzzy rules from trained RBF networks.
· Fuzzy logic based tuning of neural network training parameters.
· Fuzzy logic criteria for increasing a network size.
· Realising fuzzy membership function through clustering algorithms in unsupervised learning in SOMs and neural networks.
· Representing fuzzification, fuzzy inference and defuzzification through multi-layers feed-forward connectionist networks.
|Adaptive neuro-fuzzy modeling for prediction of ambient CO concentration at urban intersections and roadways|
A recent research line addresses the data stream mining case, where neuro-fuzzy systems are sequentially updated with new incoming samples on demand and on-the-fly. Thereby, system updates do not only include a recursive adaptation of model parameters, but also a dynamic evolution and pruning of model components (neurons, rules), in order to handle concept drift and dynamically changing system behaviour adequately and to keep the systems/models "up-to-date" anytime.
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