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Mathematical Problems in Engineering
Volume 2008, Article ID 783278, 9 pages
Research Article

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

Shanghai University of Engineering Science, Shanghai 200065, China

Received 9 May 2008; Accepted 27 July 2008

Academic Editor: Carlo Cattani

Copyright © 2008 Wushan Cheng. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.