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.
|Number of pages||6|
|Journal||Proceedings of the IEEE Conference on Decision and Control|
|State||Published - 2003|
|Event||42nd IEEE Conference on Decision and Control - Maui, HI, United States|
Duration: 9 Dec 2003 → 12 Dec 2003