文献类型：期刊文献

英文题名：Predicting electrical power output by using Granular Computing based Neuro-Fuzzy modeling method

作者：Sun, Wenyue[1]; Zhang, Jianhua[1]; Wang, Rubin[2]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China; [2] School of Science, East China University of Science and Technology, Shanghai, China

年份：2015

起止页码：2865

外文期刊名：Proceedings of the 2015 27th Chinese Control and Decision Conference, CCDC 2015

收录：EI(收录号：20154401482943)

语种：英文

摘要：The accurate prediction of electrical power output is crucial to reduce the cost for the power plant. Granular Computing (GrC) is a new data mining method. It can combine objects which have the similar characteristics to form granules. In such procedure, the core information can be extracted while the redundant information and the complexity of target problem are both reduced. In this paper, GrC is used to extract relational information and the data characteristics of a complex multidimensional data set. The extracted knowledge is translated into an initial fuzzy system and the parameters of the system are optimized by using the Adaptive Neuro-Fuzzy Inference System (ANFIS) learning methods. The use of GrC based Neuro-Fuzzy modeling (GrC-NF) can not only reduce the complexity of the target problem but also keep the interpretability characteristics of fuzzy logic. Moreover, the use of ANFIS can improve the performance of the model. Finally, a model for predicting electrical power output is built. The result comparison demonstrates the superiority of the method. ? 2015 IEEE.