Neural network implementation of a new fuzzy system
Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with host publication) › peer-review
Author(s)
Detail(s)
| Original language | English |
|---|---|
| Title of host publication | Australian and New Zealand Conference on Intelligent Information Systems - Proceedings |
| Pages | 194-198 |
| Publication status | Published - 1994 |
| Externally published | Yes |
Conference
| Title | Proceedings of the 2nd Australian and New Zealand Conference on Intelligent Information Systems |
|---|---|
| City | Brisbane, Aust |
| Period | 29 November - 2 December 1994 |
Link(s)
Abstract
The architecture and learning scheme of a new fuzzy logic system implemented in the framework of neural network is proposed. The proposed network can construct its rules and optimized its membership functions by training data pairs. Both back error propagation and least squares estimation are applied to the learning scheme. The convergence of training is expected to be faster since the least squares estimation is applied to the estimation of the consequence parameters of the system and back propagation is applied only to the estimation of premise parameters. Due to new architecture, even a high order fuzzy system can be implemented with this learning scheme. In our simulation, the proposed network is employed to model nonlinear functions.
Citation Format(s)
Neural network implementation of a new fuzzy system. / Chak, Chu Kwong; Feng, Gang.
Australian and New Zealand Conference on Intelligent Information Systems - Proceedings. 1994. p. 194-198.
Australian and New Zealand Conference on Intelligent Information Systems - Proceedings. 1994. p. 194-198.
Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with host publication) › peer-review
