Fuzzy neural logic network and its learning algorithms
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Detail(s)
| Original language | English |
|---|---|
| Article number | 183918 |
| Pages (from-to) | 476-485 |
| Journal / Publication | Proceedings of the Annual Hawaii International Conference on System Sciences |
| Volume | 1 |
| Publication status | Published - 1991 |
| Externally published | Yes |
Conference
| Title | 24th Annual Hawaii International Conference on System Sciences, HICSS 1991 |
|---|---|
| Place | United States |
| City | Kauai |
| Period | 8 - 11 January 1991 |
Link(s)
Abstract
The paper introduces the basic features of fuzzy neural logic network. Each fuzzy neural logic network model is trained from a set of knowledge in the form of examples using one of the three learning algorithms introduced. These three learning algorithms are the delta rule controlled learning algorithm and two mathematical construction algorithms, namely, the local learning method and the global learning method. Once the fuzzy neural logic network model is constructed, it is ready to accept any unknown input from the user. With a low percentage of mismatched features, output solution can be obtained.
Bibliographic Note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to lbscholars@cityu.edu.hk.
Citation Format(s)
Fuzzy neural logic network and its learning algorithms. / Chan, Sing-Chai; Nah, Fui-Hoon.
In: Proceedings of the Annual Hawaii International Conference on System Sciences, Vol. 1, 183918, 1991, p. 476-485.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
