中文摘要：

以选矿中的浮选生产过程为研究对象,提出一种基于混沌蚁群神经网络算法预测浮选过程经济技术指标的测量模型.采用主元分析进行输入数据集降维,应用混沌蚁群算法与最小二乘法相结合的混合算法调整前提参数和目标值,以取代二次规划求解优化问题,并达到求解速度快、仿真精度高的效果;同时,采用混沌蚁群算法训练神经网络,在随机扰动或测量噪声存在的情况下仍可以达到较好的训练目的,并提高了网络参数辨识的收敛速度.同时,以某实际选矿浮选生产过程的生产数据作为建模和预测数据进行仿真分析,并与初始的主元分析-反向传播（BP）神经网络模型预测结果加以对比.结果表明,所提出的模型能够实现浮选过程经济技术指标的全局预测,与优化前的模型相比其预测误差明显较低,预测精度提高了1.8%,满足优化浮选药剂添加的计算要求.

英文摘要：

A chaotic ant colony neural network model was proposed to predict the flotation process measurement model based on economic and technical indicators.This model set dimensionality reduction of input data using the principal component analysis to,and adjusted the premise parameters and target values using the hybrid chaos ant colony algorithm and least squares method.The algorithm replaces the quadratic programming optimization problem solving with high speed and accuracy of simulation results.Meanwhile,the production data of an actual flotation process was used for modeling and the simulation test.Then,the simulation results were compared with those of the PCA-BP model.The results show that the proposed model can achieve the global prediction of economic and technical indicators of the flotationprocess.Its prediction error is significantly lower,and the forecast accuracy is increased by 1.8%.The proposed model can meet the requirements of optimization calculation of flotation reagent addition.