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 t
erm 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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