The Study of Optimizing Model Based on Data Cluster of Information Fusion of Multiple Parameters

Abstract

In complex process of industrial production, it need deal with a large number of data, multiple dimensions, and generate complex data. If the neural network control indirect used, it is easy that lead to some shortcomings, such as inaccurate results and training stage of neural network lack convergence and so forth. In response to these circumstances, the integration model of data optimize processing algorithms is put forward, which is the survival of the fittest each other of dynamic K-means improve cluster algorithm and fuzzy c mean value clustering. Through two clusters to process complex data, in order that obtain accurate cluster quantity and membership. Finally through the simulation of the coal mining product data, the results proof the validity of the model.