Incremental learning model based on an improved CKS-PFNN for aluminium electrolysis manufacturing

Filtering neural networks (FNNs) are popular computing frameworks for process system modeling. However, they are vulnerable to non-Gaussian noise and consequently may suffer from low filtering accuracy. To overcome the problem, in this paper, a novel model construction algorithm by combining the improved clustering kernel function smoothing technique and the particle filter neural network (ICKS-PFNN) is proposed. Specifically, ICKS-PFNN firstly presents a construction framework for particle filter neural network (PFNN), which utilizes the dynamic approximation of particles to adjust the NN’s weights and thresholds in real time. Then, the proposed model uses kernel fuzzy C-means algorithm to uncover clusters in the particles of PFNN. A novel proportional distribution sampling strategy is adopted to maintain the diversity in particle clusters, through merging the inferior and superior particles to generate new particles based on the set proportional factors, rather than directly eliminating particles. At last, the estimation of the PFNN model is achieved by utilizing a kernel function smoothing method to update the particles in each cluster. The proposed model has been tested on the real-world system for aluminium electrolysis manufacturing and compared with several closely related frameworks. The experimental results show ICKS-PFNN obtains a superb performance when compared with other baselines. ICKS-PFNN is able to tackle noise and improve the prediction accuracy when dealing with non-Gaussian systems. Successfully applying the proposed framework in aluminium electrolysis manufacturing broadens the practical impact of FNN systems.