【摘要】 针对现有软测量建模方法中存在的不足,将修改后的适用于回归问题的Learn++算法应用到软测量建模中.Learn++算法不但保留了常用集成算法能够提高单一学习机性能的特点,还能够克服现有软测量学习方法中容易遗忘已学信息和由于重复使用原始训练数据造成时间和资源浪费的缺点,并具有增量学习能力.在建模过程中根据ELM学习速度快、简单易行、泛化能力强等特点将其选择为基本弱学习机.将基于Learn++的方法应用到LF炉钢水温度软测量建模中,实验结果表明该软测量模型具有较高的精度,可以满足实际生产的需要.更多还原

【Abstract】 A modified Learn++ algorithm adaptable to regression problems is applied to modeling soft sensors to rise above the defects in conventional method of soft sensor modeling.In the Learn++ algorithm not only the conventional integrated algorithm can be kept up to improve the performance of single learner,but the drawbacks that the learnt information is easy to be forgotten by existing learning method of soft sensors and the waste of time/resource due to using repeatedly original training data can both be overcome.Learn++ algorithm thus has incremental learnability.ELM algorithm is selected as a "weak learner" in modeling process because it learns faster and simpler and has higher generalizability than conventional neural network,thus getting rid of the limitation of local minimization and overfitting.The new modeling method based on Learn++ has been used to model the soft sensors for molten steel temperature in LF,and the results revealed that its high accuracy can meet actual requirement in production.更多还原