Application of Asymmetric Fuzzy Linear Programming in EIT

2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Pub Date : 2019-05-01 DOI:10.1109/I2MTC.2019.8827143

Mingliang Ding, Shihong Yue, Jia Li, Yaru Wang, Xiaofeng Gao, Huaxiang Wang

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引用次数: 1

Abstract

There are inconsistent, uncertain and incomplete characteristics in Electrical impedance tomography (EIT) due to two natural problems of ill-posedness and ‘soft field’ effect. The traditional EIT inverse problem solving algorithms are based on deterministic objective functions and constraints which cannot efficiently represent the natural characteristics in the EIT process. Consequently, the EIT images have low spatial resolution. Inversely, the fuzzy set theory has been validated to represent imprecise data. To represent the natural characteristics in the EIT process, an asymmetric fuzzy linear programming (AFLP) model is applied to find the optimal solution for EIT imaging process. Moreover, the parameters in AFLP are alternatively optimized. Experimental results show that AFLP has better imaging effect and higher robustness than the traditional algorithms. Compared with the existing symmetrical fuzzy linear programming (SFLP) algorithm, AFLP algorithm has high resolution for both discrete and continuous objects. These results show that AFLP algorithm provides an effective solution to enhance the EIT imaging resolution.

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不对称模糊线性规划在企业it中的应用

电阻抗层析成像(EIT)由于固有的病态和“软场”效应，存在特征不一致、不确定和不完整的问题。传统的EIT反问题求解算法基于确定性的目标函数和约束，不能有效地表征EIT过程的自然特征。因此，EIT图像的空间分辨率较低。相反，模糊集理论已被证明可以表示不精确的数据。为了反映EIT成像过程的自然特征，采用非对称模糊线性规划模型求解EIT成像过程的最优解。此外，AFLP中的参数进行了交替优化。实验结果表明，与传统算法相比，AFLP具有更好的成像效果和更高的鲁棒性。与现有的对称模糊线性规划(SFLP)算法相比，AFLP算法对离散和连续目标都具有较高的分辨率。结果表明，AFLP算法为提高EIT成像分辨率提供了一种有效的解决方案。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

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