基于改进核模糊聚类算法的软测量建模研究

针对发酵过程软测量建模采用单模型建模方法存在计算量大和精度较差的问题,提出一种基于改进核模糊聚类算法的多模型神经网络软测量建模方法.该方法首先使用主元分析方法对样本数据进行数据处理,所得主元变量作为模型的输入变量,然后使用基于粒子群优化算法的核模糊C均值聚类算法(PSKFCM)对数据集作聚类划分,最后针对每个聚类建立局部神经网络模型,多个局部神经网络模型估计结果的融合即为软测量模型的输出.将所提建模方法应用于红霉素发酵过程生物量浓度软测量建模,结果表明所建软测量模型具有较高的精度和良好的泛化能力.

Soft sensor modeling based on modified kernel fuzzy clustering algorithm

With massive data of a fermentation process, a single data-based soft sensor modeling method suffers from heavy burden calculation and poor accuracy. A novel soft sensor using multi-model neural network (MNN) based on modified kernel fuzzy clustering is proposed. Firstly, the features of sample data are extracted and the secondary variables are determined by principal component analysis (PCA). Secondly, a kernel fuzzy c-means clustering algorithm based on particle swarm optimization (PSO) is applied to group the principal data into overlapping clusters, and neural network (NN) is used to construct sub-models based on the clusters. Finally, the estimation of every sub-model is fused by computing the weighted sum of the local models. The proposed modeling method is used to construct a novel soft sensor model for an erythromycin fermentation process. Case studies show that the peoposed approach has better performance compared with conventional single model.