Design of a Recurrent Functional Neural Fuzzy Network Using Modified Differential Evolution and Cultural Algorithm
| Autor: | Chia-Chun Weng, 翁嘉俊 |
|---|---|
| Rok vydání: | 2009 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 97 In this thesis, a recurrent functional neural fuzzy network (RFNFN) with modified differential evolution (MDE) and cultural-based modified differential evolution (CMDE) is proposed for solving prediction and control problems. The proposed RFNFN model has feedback connections added in the membership function layer that can solve temporal problems. Moreover, two efficient learning algorithms, called modified differential evolution (MDE) and cultural-based modified differential evolution (CMDE) for tuning parameters of the RFNFN. In order to increase the diversity of mutations in differential evolution, we randomly choose four individual from the population to mutation. The solution can search capacity more efficiently. In cultural based modified differential evolution (CMDE) combined the cultural algorithm and modified differential evolution. It during the evolutionary process, the belief spaces extraction and use of the information is very effective in increase the performance. Simulation results show that the converging speed and root mean square error (RMSE) of the proposed method has a better performance than those of other methods. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
| Externí odkaz: |
|