Affiliations: Department of Air Transportation Management, Aletheia University, Tainan City, Taiwan, R.O.C. | Department of Aeronautics and Astronautics, National Cheng Kung University, Tainan City, Taiwan, R.O.C.
Note: [] Corresponding author. Chuen-Jyh Chen, Department of Air Transportation Management, Aletheia University, No.70-11, Beishiliao, Madou Dist., Tainan City 721, Taiwan, R.O.C. Tel.: +886 6 5703100; Ext.: 7440; Fax.: +886 6 5703834; E-mail: cjchen@mail.au.edu.tw
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.
