A Novel NN-Based Soft Sensor Based on Modified Fuzzy Kernel Clustering for Fermentation Process

With massive data of a fermentation process, a single data-based soft-sensor modeling method suffers from heavy features and bad accuracy. A novel soft sensor using multi-model neural network (MNN) based on modified kernel fuzzy clustering is proposed. Firstly, features of sample data are extracted using principal component analysis (PCA) and the secondary variables are determined by 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 approach has better performance compared to the conventional single model.