針對SMB 色譜分離過程中組分純度難于實時在線測量的問題,建立了一種基于BP 神經網絡的軟測量模型。首先對實際生產中的原始數據，經過過失誤差剔除及濾波處理后得到一套訓練數據和校驗數據組成訓練樣本，然后采用BP 神經網絡進行訓練，得到組分純度的非參數模型。為加快網絡收斂速度，采用改進BP算法對其進行訓練。在MATLAB 工作平臺上進行了大量的仿真研究，對該模型進行驗證，仿真結果表明，該
方法的有效性。
關鍵詞：模擬移動床;軟測量;人工神經元網絡;BP 算法;改進BP 算法
Abstract: In order to realize the online measurement in SMB separation process, a soft sensor model based on BP neural network which measures two components purity is built up. First by filter process of practical primal data, the training samples are gained. Then the nonparametric model on component purity is developed by use of BP neural network. Improved BP algorithm is applied to fast convergence. Much computer simulation on Matlab validates this method.
Keywords: Simulated moving bed; Soft-sensor; Artificial neural network (ANN); BP algorithm; Improved BP algorithm