IEEE Trans Neural Netw Learn Syst. 2017 Jun;28(6):1386-1396. doi: 10.1109/TNNLS.2016.2542866. Epub 2016 Mar 30.
In this paper, a novel variable selection method for neural network that can be applied to describe nonlinear industrial processes is developed. The proposed method is an iterative two-step approach. First, a multilayer perceptron is constructed. Second, the least absolute shrinkage and selection operator is introduced to select the input variables that are truly essential to the model with the shrinkage parameter is determined using a cross-validation method. Then, variables whose input weights are zero are eliminated from the data set. The algorithm is repeated until there is no improvement in the model accuracy. Simulation examples as well as an industrial application in a crude distillation unit are used to validate the proposed algorithm. The results show that the proposed approach can be used to construct a more compressed model, which incorporates a higher level of prediction accuracy than other existing methods.
本文提出了一种新的神经网络变量选择方法，可用于描述非线性工业过程。该方法是一种迭代两步法。首先，构建一个多层感知器。其次，引入最小绝对收缩和选择算子（LASSO），以选择对模型真正重要的输入变量，其中收缩参数使用交叉验证方法确定。然后，从数据集中删除输入权重为零的变量。算法重复执行，直到模型准确性没有提高为止。通过仿真示例以及在原油蒸馏装置中的工业应用验证了所提出的算法。结果表明，该方法可用于构建更紧凑的模型，其预测精度高于其他现有方法。
