利用软测量的思想，采用神经网络建立软测量模型和小波分析进行数据处理，以克服传统方法的不足，提高出口带钢厚度预测的精度。
The idea of soft-measurement is used in our work and neural network and wavelet transform are adopted to overcome deficiency of traditional method and improve forecast accuracy of exit thickness.
根据几个模型相加可提高模型的预测精度及鲁棒性的思想，提出了一种非线性软测量建模的新方法。
Inspired by the idea of combining models to improve prediction accuracy and robustness, a new method for nonlinear soft sensing modeling of chemical processes is proposed.
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