Premium
Fault Diagnosis Based on the Fuzzy‐Recurrent Neural Network
Author(s) -
Xiang Zhao,
Deyun Xiao
Publication year - 2001
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1111/j.1934-6093.2001.tb00048.x
Subject(s) - feed forward , feedforward neural network , computer science , property (philosophy) , fuzzy logic , artificial neural network , artificial intelligence , construct (python library) , fault (geology) , neuro fuzzy , recurrent neural network , process (computing) , machine learning , time delay neural network , probabilistic neural network , fuzzy control system , control engineering , engineering , philosophy , epistemology , seismology , programming language , geology , operating system
A fuzzy‐recurrent neural network (FRNN) has been constructed by adding some feedback connections to a feedforward fuzzy neural network (FNN). The FRNN expands the modeling ability of a FNN in order to deal with temporal problems. A basic concept of the FRNN is first to use process or expert knowledge, including appropriate fuzzy logic rules and membership functions, to construct an initial structure and to then use parameter‐learning algorithms to fine‐tune the membership functions and other parameters. Its recurrent property makes it suitable for dealing with temporal problems, such as on‐line fault diagnosis. In addition, it also provides human‐understandable meaning to the normal feedforward multilayer neural network, in which the internal units are always opaque to users. In a word, the trained FRNN has good interpreting ability and one‐step‐ahead predicting ability. To demonstrate the performance of the FRNN in diagnosis, a comparison is made with a conventional feedforward network. The efficiency of the FRNN is verified by the results.