Condition division method for complex processes based on the Modified Fuzzy C-Means clustering algorithm
Guan Shouping, Yan Yan · 2013
A kind of Modified Fuzzy C-Means (MFCM) clustering algorithm is presented to improve the problems of the conventional Fuzzy C-Means (FCM) clustering algorithm from three aspects: the way of clustering centre selection, application of the method of weighted dot density and the theory of information granularity. Then this new algorithm MFCM solves the problems suffer from FCM algorithm such as the sensitivity to initial value, the slow convergence speed, the possibility to fall into local optimal solution, the lost of best clustering number and equivalence partition and so on. Based on MFCM algorithm, a new condition division method for complex processes is proposed and applied to the glutamic acid fermentation process. The satisfactory simulation results are obtained and illustrated in the end of the paper.