摘要
填料塔在工业生产中应用广泛,准确预测填料塔的液泛气速具有重要的应用价值。实际的填料类型多种多样,获取的填料数据也存在差异,单一全局模型的预测效果受到一定的限制。首先给出了岭参数极限学习机模型及其节点增加的递推算法,以有效更新在线模型。结合即时学习方式,提出了局部递推岭参数极限学习机在线建模方法,用于填料塔液泛气速的预测。实验结果表明所提出方法能更充分挖掘数据间的相关信息,预测效果优于相应的全局模型。
Packed towers have been widely used in industrial productions. It is important to accurately predict the flooding velocity of packed towers. In industrial practice, there are many kinds of packings which can show different characteristics. Only using a single global model is still difficult to achieve satisfied prediction results. To overcome the problem, a new local modeling method is proposed to predict the flooding velocity. First, a recursive algorithm of ridge extreme learning machine with nodes growing is formulated, which can update the online model in an efficient manner. Moreover, using the just-in-time learning manner, the local recursive ridge parameter extreme learning machine （LRRELM）-based online modeling method is proposed. The experimental results show that the LRRELM model can explore more related information among data and thus to obtain better and more reliable prediction performance, compared with the related global models.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2016年第3期1070-1075,共6页
CIESC Journal
基金
国家自然科学基金项目(61273069)
中央高校基本科研业务费专项(JB-ZR1204)~~
关键词
非线性系统
动态建模
神经网络
递推算法
极限学习机
系统工程
nonlinear systems
dynamic modeling
neural networks
recursive algorithm
extreme learningmachine
systems engineering
