https://scholars.lib.ntu.edu.tw/handle/123456789/409700
Title: | Identification of multiple linear models for nonlinear processes | Authors: | Chen C.-L. Hsu S.-H. Lin W.-K. Wang T.-C. |
Keywords: | Fuzzy set;Identification;Linear model;Nonlinear process | Issue Date: | 2000 | Journal Volume: | 31 | Journal Issue: | 3 | Start page/Pages: | 283-293 | Source: | Journal of the Chinese Institute of Chemical Engineers | Abstract: | This work presents a nonlinear dynamic model based on several local linear models under different operating conditions. Response of the global nonlinear dynamic model is also derived by weighting the sum of all local linear model outputs. In addition, the fuzzy set theory is applied to account for the weighting factors for the local models. Also presented herein are two novel means of estimating the multiple linear models' output: The parameter interpolation method and the output difference interpolation method. According to our results, these two methods are identical in terms of interpolating the difference of state vector, outputs, and inputs. Some major identification methods, e.g., linearization of the first-principle model, identification of linear local models, and least squares algorithm, are proposed. Several typical nonlinear processes are used to demonstrate the effectiveness of the multiple linear model identification.This work presents a nonlinear dynamic model based on several local linear models under different operating conditions. Response of the global nonlinear dynamic model is also derived by weighting the sum of all local linear model outputs. In addition, the fuzzy set theory is applied to account for the weighting factors for the local models. Also presented herein are two novel means of estimating the multiple linear models' output: the parameter interpolation method and the output difference interpolation method. According to our results, these two methods are identical in terms of interpolating the difference of state vector, outputs, and inputs. Some major identification methods, e.g., linearization of the first-principle model, identification of linear local models, and least squares algorithm, are proposed. Several typical nonlinear processes are used to demonstrate the effectiveness of the multiple linear model identification. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/409700 | ISSN: | 03681653 |
Appears in Collections: | 化學工程學系 |
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