Mathematical Problems in Engineering
Volume 2008 (2008), Article ID 783278, 9 pages
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.