https://scholars.lib.ntu.edu.tw/handle/123456789/105416
標題: | Fuzzy principal component regression (FPCR) for fuzzy input and output data | 作者: | Huang, Jih-Jeng Tzeng, Gwo-Hshiung Ong, Chorng-Shyong |
關鍵字: | Fuzzy centers principal component analysis; Fuzzy principal component regression (FPCR); Fuzzy principal component scores; Fuzzy regression; Multicollinearity | 公開日期: | 2006 | 卷: | 14 | 期: | 1 | 起(迄)頁: | 87-100 | 來源出版物: | International Journal of Uncertainty Fuzziness and Knowledge-Based Systems | 摘要: | Although fuzzy regression is widely employed to solve many problems in practice, what seems to be lacking is the problem of multicollmearity. In this paper, the fuzzy centers principal component analysis is proposed to first derive the fuzzy principal component scores. Then the fuzzy principal component regression (FPCR) is formed to overcome the problem of multicollinearity in the fuzzy regression model. In addition, a numerical example is used to demonstrate the proposed method and compare with other methods. On the basis of the results, we can conclude that the proposed method can provide a correct fuzzy regression model and avoid the problem of multicollinearity. © World Scientific Publishing Company. |
URI: | http://ntur.lib.ntu.edu.tw//handle/246246/84960 https://www.scopus.com/inward/record.uri?eid=2-s2.0-33644618268&doi=10.1142%2fS0218488506003856&partnerID=40&md5=ec9652d0f1929e3ba27f13c8b7d3886b |
ISSN: | 02184885 | SDG/關鍵字: | Data reduction; Fuzzy sets; Mathematical models; Problem solving; Regression analysis; Fuzzy centers principal component analysis; Fuzzy principal component regression (FPCR); Fuzzy principal component scores; Fuzzy regression; Multicollinearity; Principal component analysis |
顯示於: | 資訊管理學系 |
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