基于多目标分层遗传模糊建模的磨矿过程溢流粒度软测量  

Multi-objective hierarchical genetic fuzzy modeling for soft-sensor of overflow particle size in grinding process

作　　者：赵珺[1] 崔庆磊 刘颖[1] 王伟[1] 

机构地区：[1]大连理工大学控制科学与工程学院,辽宁大连116023

出　　处：《控制与决策》2015年第12期2187-2192,共6页Control and Decision

基　　金：国家自然科学基金项目(61273037;61304213;61473056);国家863计划项目(2013AA040703);中央高校基本科研业务费专项资金项目(DUT13RC203)

摘　　要：提出一种基于多目标分层遗传算法的模糊系统对溢流粒度进行软测量,该方法将模糊系统分为4层,即输入层、隶属度层、规则库层和系统集成层.为了达到各层共同进化的目的,设计遗传算法各层编码策略,构建基于平均绝对百分误差和均方根误差的优化目标函数,并采用该函数计算各层个体的适应度.鉴于模糊模型训练过程中可能出现异常解,将L-M贝叶斯正则化方法融入训练过程.对磨矿生产数据的仿真实验验证了所提出方法的有效性.A fuzzy system based on multi-objective hierarchical genetic method is proposed to measure the overflow particle size. The fuzzy system are divided into four layers： the input layer, the membership layer, the rule base layer and the system layer. In order to achieve the purpose of co-evolution for each layer, a coding strategy for each layer is designed here. The mean absolute percentage error（MAPE） and root mean square error（RMSE） are considered as the optimization target to calculate the fitness value of each individual. A L-M Bayesian regularization algorithm is used for training the fuzzy system to avoid the ill-conditioned solution. The experimental results using a series of practical production data coming from a grinding plant show the effectiveness of the proposed method.

关 键 词：溢流粒度 软测量 多目标分层遗传算法 L-M贝叶斯正则化 

分 类 号：TP206[自动化与计算机技术—检测技术与自动化装置]