文献类型：期刊文献

英文题名：A review on type-2 fuzzy neural networks for system identification

作者：Tavoosi, Jafar[1];Mohammadzadeh, Ardashir[2];Jermsittiparsert, Kittisak[3,4,5]

第一作者：Tavoosi, Jafar

通讯作者：Jermsittiparsert, K[1];Jermsittiparsert, K[2];Jermsittiparsert, K[3]

机构：[1]Ilam Univ, Fac Engn, Dept Elect Engn, Ilam, Iran;[2]Univ Bonab, Fac Engn, Dept Elect Engn, Bonab, Iran;[3]Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam;[4]Duy Tan Univ, Fac Humanities & Social Sci, Da Nang 550000, Vietnam;[5]Henan Univ Econ & Law, MBA Sch, Zhengzhou 450046, Henan, Peoples R China

第一机构：Ilam Univ, Fac Engn, Dept Elect Engn, Ilam, Iran

通讯机构：[1]corresponding author), Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam;[2]corresponding author), Duy Tan Univ, Fac Humanities & Social Sci, Da Nang 550000, Vietnam;[3]corresponding author), Henan Univ Econ & Law, MBA Sch, Zhengzhou 450046, Henan, Peoples R China.|[1048419]河南财经政法大学MBA学院;[10484]河南财经政法大学;

年份：2021

卷号：25

期号：10

起止页码：7197-7212

外文期刊名：SOFT COMPUTING

收录：;EI(收录号：20211110076471);Scopus(收录号：2-s2.0-85102242343);WOS:【SCI-EXPANDED(收录号:WOS:000626804000001)】；

语种：英文

外文关键词：Type-2 fuzzy logic; Fuzzy neural networks; System identification; Review

摘要：In many engineering problems, the systems dynamics are uncertain, and then, the accurate dynamic modeling is required. Type-2 fuzzy neural networks (T2F-NNs) are extensively used in system identification problems, because of their strong estimation capability. In this paper, the application of T2F-NNs is reviewed and classified. First, an introduction to the principles of system identification, including how to extract data from a system, persistency of excitation, preprocessing of information and data, removal of outlier data, and sorting of data to learn the T2F-NNs, is presented. Then, various learning methods for structure and parameters of the T2F-NNs are reviewed and analyzed. A number of different T2F-NNs that have been used to system identification are reviewed, and their disadvantages and advantages are described. Also, their efficiency in different applications is reviewed. Finally, we will look at the horizon ahead in this issue and analyze its challenges.