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.contributor.author | T.-T. Lee | en_US |
| dc.contributor.author | P.-S. Tseng | en_US |
| dc.date.accessioned | 2014-10-30T09:28:17Z | |
| dc.date.available | 2014-10-30T09:28:17Z | |
| dc.date.issued | 1995-05-01 | zh_TW |
| dc.description.abstract | A general methodology for constructing fuzzy membership functions via B-spline curves is proposed. By using the method of least-squares, the authors 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 a B-spline membership function (BMF). By using the local control property of a B-spline curve, the BMFs can be tuned locally during the learning process. For the control of a model car through fuzzy-neural networks, it is shown that the local tuning of BMFs can indeed reduce the number of iterations tremendously. This fuzzy-neural control of a model car is presented to illustrate the performance and applicability of the proposed method | en_US |
| dc.description.uri | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=376496 | zh_TW |
| dc.identifier | ntnulib_tp_E0604_01_053 | zh_TW |
| dc.identifier.issn | 0018-9472� | zh_TW |
| dc.identifier.uri | http://rportal.lib.ntnu.edu.tw/handle/20.500.12235/31975 | |
| dc.language | en | zh_TW |
| dc.publisher | IEEE Systems, Man, and Cybernetics Society | en_US |
| dc.relation | IEEE Transactions on Systems, Man, And Cybernetics, 25(5), 841-851. | en_US |
| dc.title | Fuzzy B-spline membership function (BMF) and its applications in fuzzy-neural control | en_US |