Abstract
ABSTRACTA 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
