基于混沌蚁群神经网络的浮选过程经济技术指标预测

发布时间：2018-01-13 15:14

本文关键词：基于混沌蚁群神经网络的浮选过程经济技术指标预测　出处：《上海交通大学学报》2016年06期 　论文类型：期刊论文

【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.
[Abstract]:Taking flotation production process in ore dressing as the research object, a measurement model based on chaotic ant colony neural network algorithm is proposed to predict the economic and technical indexes of flotation process. The principal component analysis is used to reduce the dimension of the input data set. The hybrid algorithm based on chaos ant colony algorithm and least square algorithm is used to adjust the premise parameters and target values in order to replace the quadratic programming to solve the optimization problem and achieve the result of fast solution speed and high simulation accuracy. At the same time, using chaotic ant colony algorithm to train neural network can still achieve a better training purpose in the presence of random disturbance or measurement noise, and improve the convergence speed of network parameter identification. The production data of a practical flotation process are used as modeling and prediction data for simulation and analysis. The results are compared with those of the initial PCA / BP neural network model. The results show that the proposed model can achieve the global prediction of the economic and technical indexes of flotation process. Compared with the model before optimization, the prediction error is obviously lower, and the prediction accuracy is improved by 1.8%, which meets the calculation requirements of the optimized flotation reagents.
【作者单位】：辽宁科技大学电子与信息工程学院;鞍山师范学院数学系;
【基金】：国家自然科学基金项目(61473054)资助
【分类号】：TD923;TP18
【正文快照】： 浮选是依据矿浆中各种矿物的物理化学性质差异、借助汽泡的浮力将矿浆分离成品位合格的精矿和尾矿而实现矿物的有效选别的.精矿和尾矿的品位是表征浮选过程产品质量和生产效率的重要经济技术指标,因此,将精矿和尾矿的品位控制在工艺规定的范围内具有重要意义.浮选过程具有严格

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