Intelligent Control of the Complex Technology Process Based on Adaptive Pattern Clustering and Feature Map

A kind of fuzzy neural networks FNNs based on adaptive pattern clustering and feature map APCFM is proposed to improve the property of the large delay and time varying of the sintering process. By using the density clustering and learning vector quantization LVQ , the sintering process is divided automatically into subclasses which have similar clustering center and labeled fitting number. Then these labeled subclass samples are taken into fuzzy neural network FNN to be trained; this network is used to solve the prediction problem of the burning through point BTP . Using the 707 groups of actual training process data and the FNN to train APCFM algorithm, experiments prove that the system has stronger robustness and wide generality in clustering analysis and feature extraction.