A Data-Driven Soft Sensor Based on Multilayer Perceptron Neural Network With a Double LASSO Approach
A Data-Driven Soft Sensor Based on Multilayer Perceptron Neural Network With a Double LASSO Approach
复制标题
基于双LASSO方法的多层感知神经网络的数据驱动软传感器
DOI：
10.1109/tim.2019.2947126
复制
发表时间：
2020-07-01
影响因子：
5.6
通讯作者：
Jang, Shi-Shang
中科院分区：
文献类型：
--
作者：
Fan, Yajun;Tao, Bo;Jang, Shi-Shang
在非线性工业过程中，由于缺少传感器，一些产品质量或关键变量通常难以在线自动测量。在这项工作中，一种新的数据驱动的软测量技术的基础上，多层感知器（MLP）神经网络与双最小绝对收缩和选择算子（dLASSO）的方法，命名为dLASSO-MLP，开发了一个两步的过程。首先，通过过程数据集构建MLP模型。其次，将dLASSO算法集成到模型中以解决两个冗余问题，即，输入变量冗余和模型结构冗余。该方法不仅选择了对模型最敏感的输入变量，而且通过删除冗余的隐节点简化了MLP结构，避免了模型的过拟合。此外，该方法是由模拟实例以及工业应用的数据进行验证。与其他神经网络方法相比，该方法需要较少的神经元，并提出了更好的预测性能。
In nonlinear industrial processes, some product qualities or key variables are usually difficult to measure online automatically due to the lack of sensors. In this work, a novel data-driven soft sensor technology based on a multilayer perceptron (MLP) neural network with a double least absolute shrinkage and selection operator (dLASSO) approach, named dLASSO-MLP, is developed with a two-step procedure. First, an MLP model is constructed through the process data set. Second, a dLASSO algorithm is integrated into the model to solve two redundancy problems, i.e., the input variable redundancy and the model structure redundancy. The proposed method not only selects input variables that are most sensitive to the model, but also simplifies the MLP structure by deleting redundant hidden nodes to avoid the model overfitting. In addition, the method is validated by data from simulation examples as well as an industrial application. Compared to other neural network methods, the proposed method requires fewer neurons and presents better prediction performance.