TY - JOUR
T1 - A TSK-Type Fuzzy Neural Network (TFNN) Systems for Dynamic Systems Identification
AU - Lee, Ching Hung
AU - Lai, Wei Yu
AU - Lin, Yu Ching
PY - 2003
Y1 - 2003
N2 - In this paper, a TSK-type fuzzy neural network system (TFNN) for identifying unknown dynamic systems is proposed. The TFNN system can learn its knowledge base from input-output training data. Thus, the unknown system is represented as several if-then rules with TSK-type consequent parts. The TFNN system can be randomly initialized and then trained by the back-propagation algorithm. Several examples are presented to illustrate the effectiveness of our approach. fuzzy neural network, TSK-type fuzzy systems, back-propagation algorithm, system identification.
AB - In this paper, a TSK-type fuzzy neural network system (TFNN) for identifying unknown dynamic systems is proposed. The TFNN system can learn its knowledge base from input-output training data. Thus, the unknown system is represented as several if-then rules with TSK-type consequent parts. The TFNN system can be randomly initialized and then trained by the back-propagation algorithm. Several examples are presented to illustrate the effectiveness of our approach. fuzzy neural network, TSK-type fuzzy systems, back-propagation algorithm, system identification.
UR - http://www.scopus.com/inward/record.url?scp=1542289088&partnerID=8YFLogxK
U2 - 10.1109/CDC.2003.1271776
DO - 10.1109/CDC.2003.1271776
M3 - Conference article
AN - SCOPUS:1542289088
SN - 0191-2216
VL - 4
SP - 4002
EP - 4007
JO - Proceedings of the IEEE Conference on Decision and Control
JF - Proceedings of the IEEE Conference on Decision and Control
T2 - 42nd IEEE Conference on Decision and Control
Y2 - 9 December 2003 through 12 December 2003
ER -