The application of simulated annealing K-means clustering algorithm in combination modeling

Traditional K-means clustering algorithm easily fall into local extremum. A maximum distances product algorithm is used to optimize the initial clustering centers and a K-means clustering algorithm with simulated annealing (SA) is promoted. The proposed method uses SA to optimize the clustering pattern in clustering analysis which can achieve global optimization. A combination model based on support vector machine (SVM) is established. The method is applied to a soft sensor modeling for the quality index in a Bisphenol A production process. The simulation result shows that the change trend of phenol content is tracked effectively and data classification result is improved by the algorithm. It also shows that the estimation precision of the soft sensor model is improved which demonstrates the potential application in industry field.