Huang, Jih-JengJih-JengHuangTzeng, Gwo-HshiungGwo-HshiungTzengOng, Chorng-ShyongChorng-ShyongOng2008-10-222018-06-292008-10-222018-06-29200602184885http://ntur.lib.ntu.edu.tw//handle/246246/84960https://www.scopus.com/inward/record.uri?eid=2-s2.0-33644618268&doi=10.1142%2fS0218488506003856&partnerID=40&md5=ec9652d0f1929e3ba27f13c8b7d3886bAlthough 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.application/pdf1074265 bytesapplication/pdfen-USFuzzy centers principal component analysis; Fuzzy principal component regression (FPCR); Fuzzy principal component scores; Fuzzy regression; MulticollinearityData 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 analysisjournal article2-s2.0-33644618268http://ntur.lib.ntu.edu.tw/bitstream/246246/84960/1/4.pdf