关键词: 煤粉粒度, 模糊C均值聚类, 最小二乘支持向量机回归, 软测量, 遗传算法, 变长度染色体

Abstract: Inspired by the idea that combining several models can improve the prediction accuracy and robustness on the whole, a multiple least squares-support vector machine regressor(MLS-SVRs) soft sensing modeling of the granularity of pulverized coal is proposed based on fuzzy C-means(FCM) clustering algorithm. Genetic algorithm based on sizable chromosome is introduced to optimize the number of fuzzy clustering and cluster centers. Then, the whole training data set is divided into several clusters with different centers by FCM algorithm and each subset is trained by LS-SVRs, and the degrees of membership resulting from fuzzy clustering are used to weight and summarize the outputs of all submodels, thus giving the finial soft sensing outcome. Simulation results showed that the proposed model is effective in the granularity prediction and meets the requirement of the on-line soft sensor for real-time optimization control in pulverized coal production process.