Abstract
The development of neuro-fuzzy systems by integrating neural networks and fuzzy systems is desired because such systems can adjust fuzzy membership functions and produce fuzzy inference rules by case-learning without the need for experts or experiments. It has been applied to various fields, but there has been no detailed study of the various neuro-fuzzy models applicable to rule generation. In this paper, an experimentally verified five-layer and three-phase network is presented, which shows the effectiveness with which the neuro-fuzzy system automatically determines membership functions and selects activation fuzzy rules using both system identification and vibration control examples in engineering applications.
| Original language | English |
|---|---|
| Pages (from-to) | 881-892 |
| Number of pages | 12 |
| Journal | Journal of Intelligent and Fuzzy Systems |
| Volume | 25 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2013 |
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All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Engineering(all)
- Artificial Intelligence
Cite this
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Development of a rule selection mechanism by using neuro-fuzzy methodology for structural vibration suppression. / Chen, Chuen Jyh; Yang, Shih-Ming; Chen, Chu Yun.
In: Journal of Intelligent and Fuzzy Systems, Vol. 25, No. 4, 2013, p. 881-892.Research output: Contribution to journal › Article
TY - JOUR
T1 - Development of a rule selection mechanism by using neuro-fuzzy methodology for structural vibration suppression
AU - Chen, Chuen Jyh
AU - Yang, Shih-Ming
AU - Chen, Chu Yun
PY - 2013
Y1 - 2013
N2 - The development of neuro-fuzzy systems by integrating neural networks and fuzzy systems is desired because such systems can adjust fuzzy membership functions and produce fuzzy inference rules by case-learning without the need for experts or experiments. It has been applied to various fields, but there has been no detailed study of the various neuro-fuzzy models applicable to rule generation. In this paper, an experimentally verified five-layer and three-phase network is presented, which shows the effectiveness with which the neuro-fuzzy system automatically determines membership functions and selects activation fuzzy rules using both system identification and vibration control examples in engineering applications.
AB - The development of neuro-fuzzy systems by integrating neural networks and fuzzy systems is desired because such systems can adjust fuzzy membership functions and produce fuzzy inference rules by case-learning without the need for experts or experiments. It has been applied to various fields, but there has been no detailed study of the various neuro-fuzzy models applicable to rule generation. In this paper, an experimentally verified five-layer and three-phase network is presented, which shows the effectiveness with which the neuro-fuzzy system automatically determines membership functions and selects activation fuzzy rules using both system identification and vibration control examples in engineering applications.
UR - http://www.scopus.com/inward/record.url?scp=85013617382&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85013617382&partnerID=8YFLogxK
U2 - 10.3233/IFS-120691
DO - 10.3233/IFS-120691
M3 - Article
VL - 25
SP - 881
EP - 892
JO - Journal of Intelligent and Fuzzy Systems
JF - Journal of Intelligent and Fuzzy Systems
SN - 1064-1246
IS - 4
ER -