A novel logistic-NARX model as a classifier for dynamic binary classification
A novel logistic-NARX model as a classifier for dynamic binary classification
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DOI：
10.1007/s00521-017-2976-x
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发表时间：
2017
影响因子：
6
通讯作者：
José Roberto Ayala Solares;Hua‐Liang Wei;S. Billings
中科院分区：
文献类型：
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作者：
José Roberto Ayala Solares;Hua‐Liang Wei;S. Billings
抽象系统辨识和数据驱动建模技术在过去的几十年中得到了无处不在的应用。具体地说，参数建模方法，例如具有外部输入模型(ARX和NARX)的线性和非线性自回归以及其他类似和相关的模型类型，由于其易于计算的参数中线性结构而被优选地应用于处理各种数据驱动的建模问题，这使得所得到的模型能够容易地被解释。近年来，已经提出了几种NARX方法的变体，以提高原始算法的性能。然而，在大多数情况下，NARX模型被应用于所有输出变量都涉及从连续过程中采样的连续或离散时间序列的回归问题，而对于输出信号为二进制序列的分类问题很少被关注。因此，我们开发了一种新的分类算法，将NARX方法与Logistic回归相结合，该方法被称为Logistic-NARX模型。这种组合是有利的，因为NARX方法有助于处理多重共线性问题，而Logistic回归产生预测类别的模型
Abstract System identification and data-driven modeling techniques have seen ubiquitous applications in the past decades. In particular, parametric modeling methodologies such as linear and nonlinear autoregressive with exogenous input models (ARX and NARX) and other similar and related model types have been preferably applied to handle diverse data-driven modeling problems due to their easy-to-compute linear-in-the-parameter structure, which allows the resultant models to be easily interpreted. In recent years, several variations of the NARX methodology have been proposed that improve the performance of the original algorithm. Nevertheless, in most cases, NARX models are applied to regression problems where all output variables involve continuous or discrete-time sequences sampled from a continuous process, and little attention has been paid to classification problems where the output signal is a binary sequence. Therefore, we developed a novel classification algorithm that combines the NARX methodology with logistic regression and the proposed method is referred to as logistic-NARX model. Such a combination is advantageous since the NARX methodology helps to deal with the multicollinearity problem while the logistic regression produces a model that predicts categorical