Fuzzy B-spline membership function (BMF) and its applications in fuzzy-neural control
| dc.contributor | 國立臺灣師範大學電機工程學系 | zh_tw |
| dc.contributor.author | C.-H. Wang | en_US |
| dc.contributor.author | W.-Y. Wang | en_US |
| dc.date.accessioned | 2014-10-30T09:28:26Z | |
| dc.date.available | 2014-10-30T09:28:26Z | |
| dc.date.issued | 1994-10-05 | zh_TW |
| dc.description.abstract | A general methodology for constructing fuzzy membership functions via B-spline curve is proposed. By using the method of least-squares, we translate the empirical data into the form of the control points of B-spline curves to construct fuzzy membership functions. This unified form of fuzzy membership functions is called as B-spline membership functions (BMF's). By using the local control property of B-spline curve, the BMF's can be tuned locally during learning process. For the control of a model car through fuzzy-neural networks, it is shown that the local tuning of BMF's can indeed reduce the number of iterations tremendously | en_US |
| dc.description.uri | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=400147 | zh_TW |
| dc.identifier | ntnulib_tp_E0604_02_092 | zh_TW |
| dc.identifier.uri | http://rportal.lib.ntnu.edu.tw/handle/20.500.12235/32069 | |
| dc.language | en | zh_TW |
| dc.relation | IEEE International Conference on Systems, Man and Cybernetics, vol. 2, San Antonio, TX,pp. 2008-2014 | en_US |
| dc.title | Fuzzy B-spline membership function (BMF) and its applications in fuzzy-neural control | en_US |