Interval Fuzzy Modeling Based on Minimizing-norm on Approximation Error

Authors

Xiaoyong Liu, College of engineering and technology, Zunyi Normal College, Zunyi 563002, China;
Zhonggang Xiong, College of engineering and technology, Zunyi Normal College, Zunyi 563002, China;
Changguo Yan, College of engineering and technology, Zunyi Normal College, Zunyi 563002, China;

Abstract

Abstract: As the obtained data in many practical applications tend to be uncertain or inaccurate, conventional modeling methods characterized by deterministic model for this type of data have become undesirable. Taking linear programming and TS fuzzy model and some ideas from norm minimization into consideration, a novel method identifying interval fuzzy model (INFUMO) consisting of upper and lower TS fuzzy model (referred to as f_U and f_L) has been studied. In order to solve INFUMO, optimization problems based on minimizing-norm with respect to approximation error corresponding to f_U and f_L are constructed. Finally, optimization problems are solved by linear programming and INFUMO is thus constructed. The proposed method not only can deal with the problem that the conventional modeling method of uncertain data usually results in deterministic model, but also has better robustness.

Recommended Citation

Liu, Xiaoyong; Xiong, Zhonggang; and Yan, Changguo (2019) "Interval Fuzzy Modeling Based on Minimizing-norm on Approximation Error," Journal of System Simulation: Vol. 30: Iss. 3, Article 54.
DOI: 10.16182/j.issn1004731x.joss.201803054
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss3/54

First Page

1203

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201803054

Last Page

1209

CLC

TP391.9

Recommended Citation

Liu Xiaoyong, Xiong Zhonggang, Yan Changguo. Interval Fuzzy Modeling Based on Minimizing-norm on Approximation Error[J]. Journal of System Simulation, 2018, 30(3): 1203-1209.

DOI

10.16182/j.issn1004731x.joss.201803054