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Pseudo Optimization of E-Nose Data Using Region Selection with Feature Feedback Based on Regularized Linear Discriminant Analysis

机译:基于正则线性判别分析的带特征反馈区域选择伪神经电子鼻数据

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In this paper, we present a pseudo optimization method for electronic nose (e-nose) data using region selection with feature feedback based on regularized linear discriminant analysis (R-LDA) to enhance the performance and cost functions of an e-nose system. To implement cost- and performance-effective e-nose systems, the number of channels, sampling time and sensing time of the e-nose must be considered. We propose a method to select both important channels and an important time-horizon by analyzing e-nose sensor data. By extending previous feature feedback results, we obtain a two-dimensional discriminant information map consisting of channels and time units by reverse mapping the feature space to the data space based on R-LDA. The discriminant information map enables optimal channels and time units to be heuristically selected to improve the performance and cost functions. The efficacy of the proposed method is demonstrated experimentally for different volatile organic compounds. In particular, our method is both cost and performance effective for the real implementation of e-nose systems.
机译:在本文中,我们提出了一种基于正则化线性判别分析(R-LDA)的具有特征反馈的区域选择和电子鼻(e-nose)数据伪优化方法,以增强电子鼻系统的性能和成本功能。为了实施具有成本效益和性能的电子鼻系统,必须考虑电子鼻的通道数量,采样时间和传感时间。我们提出了一种通过分析电子鼻传感器数据来选择重要通道和重要时间范围的方法。通过扩展先前的特征反馈结果,我们通过基于R-LDA将特征空间反向映射到数据空间,获得了由通道和时间单位组成的二维判别信息图。判别信息图可启发性地选择最佳通道和时间单位,以改善性能和成本函数。实验证明了该方法对不同挥发性有机化合物的有效性。尤其是,对于真正实施电子鼻系统,我们的方法在成本和性能上都是有效的。

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