Choice of latent explanatory variables: a multiobjective optimization approach

✍ Scribed by Danyang Liu; Sirish L. Shah; D. Grant Fisher

• Publisher: John Wiley and Sons
• Year: 2000
• Tongue: English
• Weight: 121 KB
• Volume: 14
• Category: Article
• ISSN: 0886-9383
• DOI: 10.1002/(sici)1099-128x(200003/04)14:2<79::aid-cem578>3.0.co;2-o

✦ Synopsis

A multiobjective, optimization-based approach for finding latent explanatory variables for linear models is presented. The best choice of a set of latent explanatory variables is made by minimizing a user-specified combination of criteria. In this paper, three criteria are used: (i) the data matrix-related residue, (ii) the observation-or measurement-related residue and (iii) the condition number of the new data matrix of the latent explanatory variables. Successful application of the proposed technique toward identification of a multivariable pilot-scale plant is presented. The proposed algorithm is compared with the well-known PLS algorithm, and the result shows that the proposed algorithm is better than the PLS algorithm.