基于非负绞杀与长短期记忆神经网络的动态软测量算法

现代工业过程建模中,生产过程的多变量、非线性及动态性会导致模型复杂度增高且建模精度降低.针对这一问题,将非负绞杀算法(NNG)嵌入长短期记忆(LSTM)神经网络,提出一种基于LSTM神经网络及其输入变量选择的动态软测量算法.首先,通过参数优化生成训练好的LSTM神经网络,利用其出色的历史信息记忆能力处理工业过程中的动态、时滞等问题;其次,采用NNG算法对LSTM网络输入权重进行压缩,剔除冗余变量,提高模型精度,并采用网格搜索法与分块交叉验证对其超参数寻优;最后,将算法应用于某火电厂脱硫过程排放烟气SO2浓度软测量建模,并与其它先进算法进行性能比较.实验结果表明所提算法能有效剔除冗余变量,降低模型复杂度并提高其预测性能.

Dynamic soft sensor algorithm based on nonnegative garrote and long short-term memory neural network

In modern industrial process modeling, the multivariable, nonlinearity and dynamics of the production process increase the model complexity and reduce the model accuracy. In response to this problem, a dynamic soft-sensing algorithm based on the long short-term memory (LSTM) neural network and its input variable selection is proposed by embedding the nonnegative garrote (NNG) into the LSTM neural network. First, a well-trained LSTM neural network is generated with parameter optimization, in which the dynamics and time-delay of industrial processes are handled by its excellent memory capacity of historical information. Then, the NNG algorithm is used to compress the input weights of the LSTM network to eliminate the redundant variables and improve the model accuracy. Grid search and blocked crossvalidation are used to find the optimal hyperparameter of the algorithm. Finally, the algorithm is applied to the soft-sensing modeling of SO2 concentration in the flue gas that is discharged from the desulfurization process of a thermal power plant, and the performance of the algorithm is compared with other state-of-the-art algorithms. Experimental results demonstrate that the proposed algorithm can effectively delete the redundant variables, reduce the model complexity and improve the prediction performance of the model.