期刊論文
| 學年 | 102 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2014-05-01 |
| 作品名稱 | An Approach for Fuzzy Modeling based on Self-Organizing Feature Maps Neural Network |
| 作品名稱（其他語言） | |
| 著者 | Chen, Ching-yi; Chiang, Jen-Shiun; Chen, K. Y.; Liu, T. K.; Wong, Ching-Chang |
| 單位 | 淡江大學電機工程學系 |
| 出版者 | Bahrain: Natural Sciences Publishing Corporation |
| 著錄名稱、卷期、頁數 | Applied Mathematics & Information Sciences 8(3), pp.1207-1215 |
| 摘要 | Exploration of large and high-dimensional data sets is one of the main problems in data analysis. Self-organizing feature maps (SOFM) is a powerful technique for clustering analysis and data mining. Competitive learning in the SOFM training process focuses on finding a neuron that its weight vector is most similar to that of an input vector. SOFM can be used to map large data sets to a simpler, usually one or two-dimensional topological structure. In this paper, we present a new approach to acquisition of initial fuzzy rules using SOFM learning algorithm, not only for its vector feature, but also for its topological. In general, fuzzy modeling requires two stages: structure identification and parameter learning. First, the algorithm partitions the input space into some local regions by using SOFM, then it determines the decision boundaries for local input regions, and finally, based on the decision boundaries, it learns the fuzzy rule for each local region by recursive least squares algorithm. The simulation results show that the proposed method can provide good model structure for fuzzy modeling and has high computing efficiency. |
| 關鍵字 | |
| 語言 | en_US |
| ISSN | 2325-0399 |
| 期刊性質 | 國外 |
| 收錄於 | SCI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 是 |
| 國別 | BHR |
| 公開徵稿 | |
| 出版型式 | 電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/80564 ) |