基于后向区间选择偏最小二乘算法的软测量建模

Soft-sensing modeling based on backward interval selection partial least squares algorithm

偏最小二乘是在光谱多变量校正中广泛使用的一种算法,现已经发现高效的变量选择不仅能够提高模型的预测能力,也可大大降低模型的复杂度。为了建立具有鲁棒性和低复杂度的基于光谱的在线软测量模型,考虑到光谱变量之间高度相关这一事实,提出一种基于后向区间选择策略的偏最小二乘算法。该算法主要步骤是:先将光谱波长域细分为一定数量的等长子区间;再采用后向淘汰的策略,将各个子区间逐步淘汰,形成一个淘汰序列;最后,再反向选择一定数量的子区间建立最终的模型。通过一个实例以及与传统基于全谱的偏最小二乘算法比较,显示出了该算法的在建立软测量模型方面的优良性能。

Partial least squares （PLS） is a popular method applied widely to the multi-component spectral calibration, it has been recognized that an efficient feature selection can be highly beneficial both to improve the predictive ability of the model and to greatly reduce its complexity. Considering the fact that there is a high probability of autocorrelation among the neighboring variables of spectra, in order to establish on-line spectral soft- sensing model with robustness and parsimony, an algorithm called backward interval selection partial least squares algorithm （BISPLS） is proposed. The main steps of this algorithm is to split the spectra into smaller equidistant subinterval first and, afterwards,eliminate all the subintervals step by step using the backward strategy,result in a sequence of subintervals,which make it possible to establish a final model by using a few subintervals. By a real example and the comparison with full-spectrum PLS, the algorithm＇ s advantages are showed in soft-sensor modeling.