In this thesis, M-estimators with Ramsay¡¦s function used in robust regression theory for linear parametric regression problems will be generalized to nonparametric Ramsay fuzzy neural networks (RFNNs) for nonlinear regression problems. Emphasis is put particularly on the robustness against outliers. This provides alternative learning machines when faced with general nonlinear learning problems. Simple weight updating rules based on incremental gradient descent and iteratively reweighted least squares (IRLS) will be derived. Some numerical examples will be provided to compare the robustness against outliers for usual fuzzy neural networks (FNNs) and the proposed RFNNs. Simulation results show that the RFNNs proposed in this thesis have good robustness against outliers.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0623108-234132 |
| Date | 23 June 2008 |
| Creators | Wu, Tzung-Han |
| Contributors | Jer-Guang Hsieh, Jeng-Yih Juang, Jyh-Horng Jeng, Chang-Hua Lien, Tsu-Tian Lee |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0623108-234132 |
| Rights | unrestricted, Copyright information available at source archive |
Page generated in 0.0022 seconds