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首页> 外文期刊>Journal of natural gas science and engineering >Robust data-driven soft sensor based on iteratively weighted least squares support vector regression optimized by the cuckoo optimization algorithm
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Robust data-driven soft sensor based on iteratively weighted least squares support vector regression optimized by the cuckoo optimization algorithm

机译：基于布谷鸟优化算法优化的基于迭代加权最小二乘支持向量回归的鲁棒数据驱动软传感器

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In process industries, use of the data-driven soft sensors for the purpose of process control and monitoring has gained much popularity. Data-driven soft sensors infer the process quality variables from the available historical process data. A considerable amount of process data such as pressures, temperatures, etc., are measured routinely and stored permanently. However, the quality of these data often varies. Measurement noises and data outliers are the most common effects which lead to poor quality of process data. Application of standard statistical techniques to operate data may lead to model deterioration due to contaminating observations. Therefore, the objective of this paper is to present a robust approach for the development of data-driven soft sensors. In this paper, the modeling method that is used to develop soft sensor is a combination of Nonlinear Auto Regressive with exogenous inputs (NARX) structure with Least Squares Support Vector Regression (LSSVR). The LSSVRs' parameters are optimized by a new evolutionary optimization technique known as the Cuckoo Optimization Algorithm (COA). Then in order to make the soft sensor robust against the data outliers and noises especially the long tail noises, a new approach is proposed. The proposed method is based on the Iteratively Weighted LSSVR (IWLSSVR) which uses the Myriad weighting function. The proposed approach was applied to the prediction of the n-butane (C4) concentration in a debutanizer column unit. The technique was consequently compared against the conventional LSSVR algorithm which is based on the quadratic loss function. It turns out that reweighting the LSSVR estimate using the Myriad weight function improves the performance of the LSSVR-based soft sensor when noises and outliers exist in the measured data. The designed robust soft sensor is also compared with another robust soft sensor which is recently developed based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) optimized by the Particle Swarm Optimization (PSO). The simulation results show that the designed IWLSSVR-based soft sensor is more robust when the measured data have some impurities. (C) 2014 Elsevier B.V. All rights reserved.
机译：在过程工业中，将数据驱动的软传感器用于过程控制和监视已广受欢迎。数据驱动的软传感器从可用的历史过程数据中推断过程质量变量。例行测量并永久存储大量过程数据，例如压力，温度等。但是，这些数据的质量通常会有所不同。测量噪声和数据离群值是导致过程数据质量较差的最常见影响。应用标准统计技术操作数据可能会由于污染的观察结果而导致模型恶化。因此，本文的目的是为数据驱动的软传感器的开发提出一种可靠的方法。在本文中，用于开发软传感器的建模方法是将非线性自回归与外来输入（NARX）结构与最小二乘支持向量回归（LSSVR）相结合。 LSSVR的参数通过一种称为“布谷鸟优化算法”（COA）的新进化优化技术进行优化。然后，为了使软传感器对数据离群值和噪声特别是长尾噪声具有鲁棒性，提出了一种新的方法。所提出的方法基于使用无数加权功能的迭代加权LSSVR（IWLSSVR）。所提出的方法被用于预测丁烷塔塔塔单元中正丁烷（C4）的浓度。因此，将该技术与传统的基于二次损失函数的LSSVR算法进行了比较。事实证明，当测量数据中存在噪声和异常值时，使用无数权重函数对LSSVR估计值进行重新加权可以提高基于LSSVR的软传感器的性能。还将设计的鲁棒性软传感器与最近基于粒子群优化（PSO）优化的自适应神经模糊推理系统（ANFIS）开发的另一种鲁棒性软传感器进行了比较。仿真结果表明，所设计的基于IWLSSVR的软传感器在测量数据中存在某些杂质时更加鲁棒。 （C）2014 Elsevier B.V.保留所有权利。

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