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- W2126989360 abstract "In this paper, the intelligent techniques are utilized to enhance the quality control precision in the steel strip cold rolling production. Firstly, a new control scheme is proposed, establishing the classifier of the steel strip flatness signal is the basis of the scheme. The fuzzy clustering method is used to establish the classifier. Getting the high quality clustering prototypes is one of the key tasks. Secondly, a kind of new fuzzy clustering model, generalized fuzzy C-means clustering (GeFCM) model, is proposed and used as the mining tools in the real applications. The results, under the comparisons with the results obtained by the basic fuzzy clustering model, show the GeFCM is robust and efficient and it not only can get much better clustering prototypes, which are used as the classifier, but also can easily and effectively mine the outliers. It is very helpful in the steel strip flatness quality control system in one real cold rolling line. Finally, it is pointed out that the new model's efficiency is mainly due to the introduction of a set of adaptive degrees wj (j=1...n, and n is the number the data objects) and an adaptive exponent p which jointly affect the clustering operations. In nature, the proposed GeFCM model is the generalized version of the existing fuzzy clustering models." @default.
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- W2126989360 date "2009-12-01" @default.
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- W2126989360 title "A kind of generalized fuzzy C-means clustering model and its applications in mining steel strip flatness signal" @default.
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