Dai Huangtao, Zhao Taoyan, Cao Jiangtao, Li Ping
School of Information and Control Engineering, Liaoning Petrochemical University, Fushun, 113001, China.
School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, 114051, China.
Sci Rep. 2024 Oct 10;14(1):23709. doi: 10.1038/s41598-024-75009-9.
To address the issue of low accuracy in soft sensor modeling of key variables caused by multi-variable coupling and parameter sensitivity in complex processes, this paper introduces a TSK-type-based self-evolving compensatory interval type-2 fuzzy Long short-term memory (LSTM) neural network (TSECIT2FNN-LSTM) soft sensor model. The proposed TSECIT2FNN-LSTM integrates the LSTM neural network with the interval type-2 fuzzy inference system to address long-term dependencies in sequence data by utilizing the gate mechanism of the LSTM neural network. The TSECIT2FNN-LSTM structure learning algorithm uses the firing strength of the network rule antecedent to decide whether to generate new rules to improve the rationality of the network structure. TSECIT2FNN-LSTM parameter learning utilizes the gradient descent method to optimize network parameters. However, unlike other interval type-2 fuzzy neural network gradient calculation processes, the error term in the LSTM node parameter gradient of TSECIT2FNN-LSTM is propagated backwards in the time dimension. Additionally, the error term is simultaneously transferred to the upper layer network to enhance network prediction accuracy and memory capabilities. The TSECIT2FNN-LSTM soft sensor model is utilized to predict the alcohol concentration in wine and the nitrogen oxide emission in gas turbines. Experimental results demonstrate that the proposed TSECIT2FNN-LSTM soft sensing model achieves higher prediction accuracy compared to other models.
针对复杂过程中多变量耦合和参数敏感性导致关键变量软传感器建模精度较低的问题，本文介绍了一种基于TSK型的自进化补偿区间二型模糊长短期记忆（LSTM）神经网络（TSECIT2FNN-LSTM）软传感器模型。所提出的TSECIT2FNN-LSTM将LSTM神经网络与区间二型模糊推理系统相结合，通过利用LSTM神经网络的门机制来处理序列数据中的长期依赖关系。TSECIT2FNN-LSTM结构学习算法利用网络规则前件的激发强度来决定是否生成新规则，以提高网络结构的合理性。TSECIT2FNN-LSTM参数学习采用梯度下降法来优化网络参数。然而，与其他区间二型模糊神经网络梯度计算过程不同，TSECIT2FNN-LSTM的LSTM节点参数梯度中的误差项在时间维度上反向传播。此外，误差项同时传递到上层网络，以提高网络预测精度和记忆能力。TSECIT2FNN-LSTM软传感器模型用于预测葡萄酒中的酒精浓度和燃气轮机中的氮氧化物排放。实验结果表明，所提出的TSECIT2FNN-LSTM软传感模型与其他模型相比具有更高的预测精度。
