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Aimed to the measuring problem of steam consumption in Dyeing process, a multiple neural network soft sensing modeling of Dyeing steam consumption based on adaptive fuzzy C-means clustering (FCM) is presented. The method is used for separating a whole real-time training data set into several clusters with different centers, and the clustering centers can been modified by an adaptive fuzzy clustering...
A cluster validity index for fuzzy partitions has two functions. One is to identify the number of clusters of an unlabeled data set. The other is to assess the clustering result of an unlabeled data set. The first function has been thoroughly investigated. However, no researchers attach importance to the second function. This paper devises an experiment to investigate the second function of current...
In dealing with the problem that the important parameters of a penicillin fermentation process are hard to measure precisely, such as biomass concentration and production concentration, therefore, a soft sensor modeling for the penicillin fermentation based on fuzzy c-means clustering and least square support vector machine (LS-SVM) is proposed. First of all, features of sample data are extracted...
A Hierarchical Fuzzy Clustering Algorithm is put forward to overcome the limitation of Fuzzy C-Means (FCM) algorithm. HFC discovers the high concentrated data areas by the agglomerative hierarchical clustering method quickly, analyzes and merges the data areas, and then uses the evaluation function to find the optimum clustering scheme. Experimental results indicate that HFC has higher clustering...
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