Fuzzy Hierarchical Stochastic Configuration Networks for Industrial Soft Sensor Modeling

Traditional stochastic configuration networks (SCNs)-based industrial soft sensors have the shortcomings of failing to account for “slowness” characteristic and struggling with processing rule-based information. The original slow feature extraction methods based on autoencoders with fixed structure are lack of flexibility and difficult to maintain a balance between efficiency and accuracy. To address these challenges, a framework of fuzzy hierarchical SCNs (FHSCNs) is proposed, which consists of a slow feature extraction block, a fuzzy inference block and an enhanced output block. The slow feature extraction block is designed, which utilizes a autoencoder based on two SCNs with shared-parameters and the incremental learning paradigm of SCNs to efficiently and adaptively extract the slow-varying latent features. The fuzzy inference block is proposed, which can process rule-based slow feature information. The fuzzy inference block can allow the model to have the fuzzy reasoning capabilities and improve the model interpretability. The enhanced output block with an enhancement layer and a direct-connect portion is presented, which enables the FHSCNs to have the ability of capturing both linearity and nonlinearity of the fuzzy rule-based features. The proposed framework is validated through comprehensive experiments to demonstrate its effectiveness in constructing industrial soft sensor model.