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以浮选过程关键控制量(浮选加药量)为研究对象,提出一种基于核主元分析(KPCA)和最小二乘支持向量机(LSSVM)的浮选加药控制模型。通过对浮选过程的工艺的分析,确定了模型的输入输出变量。采用KPCA算法对样本数据进行降维处理,简化模型结构,并用LSSVM建立浮选加药控制模型。采用基于高斯变异和柯西变异改进的混合蛙跳算法(ILSFA)对LSSVM模型的参数进行优化,获得高精度的控制模型。仿真结果表明,提出的模型能够显著提高出浮选加药的控制精度,满足浮选加药过程的需求。
Taking flotation critical control (flotation dosage) as the research object, a flotation dosing control model based on kernel principal component analysis (KPCA) and least square support vector machine (LSSVM) was proposed. Through the analysis of the process of the flotation process, the input and output variables of the model are determined. The KPCA algorithm was used to reduce the dimensionality of the sample data to simplify the structure of the model and to establish a flotation dosing control model with LSSVM. The parameters of LSSVM model are optimized by using the hybrid frog leaping algorithm (ILSFA) based on Gaussian mutation and Cauchy mutation, and a high-precision control model is obtained. Simulation results show that the proposed model can significantly improve the flotation dosing control accuracy to meet the needs of flotation dosing process.