摘要
对模糊C-均值聚类算法加以改进,将系统输入数据进行模糊划分,分成具有几个不同聚类中心的子集;继而引入到多模型建模过程中,针对每个子集建立相应的径向基函数(RBF)网络模型。而全局模型则由各个子模型的输出加权组合。最后通过对聚合釜反应器软测量建模的研究,表明该方法具有拟合精度高和泛化能力强的特点,验证了此多模型建模方法的有效性和快速性。
Based on fuzzy C-means clustering (FCM) algorithm, a modified clustering algorithm is proposed. Using this algorithm, the input data set of a system can be quickly divided into several fuzzy clusters with distinct centers. Then, the multiple neural network modeling is introduced to the determination process. Corresponding to different clusters, each subset can be trained by radial basic function networks (RBF), and the global model is a certain combination of these multiple models. Finally, the performance of this method is evaluated by a practical case of the soft sensor of polyester reactor, which demonstrates that it has a higher approaching precision and a stronger generalization capacity. The obtained results prove its accuracy and validity.
出处
《华东理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2005年第2期208-211,226,共5页
Journal of East China University of Science and Technology
基金
国家863资助项目(2002AA412120)
