| Language | English |
|---|---|
| Pages | 211-243 |
| Journal | Fuzzy Sets and Systems |
| Volume | 150 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 1 Mar 2005 |
Cite this
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An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network. / Leng, G; McGinnity, T; Prasad, G.
In: Fuzzy Sets and Systems, Vol. 150, No. 2, 01.03.2005, p. 211-243.Research output: Contribution to journal › Article
TY - JOUR
T1 - An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network
AU - Leng, G
AU - McGinnity, T
AU - Prasad, G
N1 - Other Details ------------------------------------ Fuzzy modeling of complex systems requires the extraction of a suitable collection of fuzzy rules from the available data set. This is often a difficult challenge. While neural networks have the ability to learn the fuzzy rules automatically, it is usually not possible to extract the rules from the trained neural network. The importance of this paper is that it presents a new self-organising fuzzy neural network that is able to extract fuzzy rules from the data set, and implement structure and parameter learning, with a high accuracy. The automatic addition and pruning techniques implemented ensure a compact network.
PY - 2005/3/1
Y1 - 2005/3/1
U2 - 10.1016/j.fss.2004.03.001
DO - 10.1016/j.fss.2004.03.001
M3 - Article
VL - 150
SP - 211
EP - 243
JO - Fuzzy Sets and Systems
T2 - Fuzzy Sets and Systems
JF - Fuzzy Sets and Systems
SN - 0165-0114
IS - 2
ER -