@inproceedings{7a67c925cf1f46c4aae2dfcc95cacb6d,
title = "Online fault diagnosis of nonlinear systems based on neurofuzzy networks",
abstract = "Artificial intelligence techniques such as neural networks and fuzzy logic have been widely used in fault detection and diagnosis. Combining these two techniques, referred to as neurofuzzy networks, provides a powerful tool for modelling. B-spline neurofuzzy networks are used to model the residuals. The weights of the networks are trained online using recursive least squares method. Fuzzy rules are extracted from the networks and they provide linguistic description of the residuals. The qualitative information of the residuals facilitates isolation of the system faults. The proposed scheme is illustrated using a simulation example of a DC motor.",
keywords = "Fault diagnosis, Fuzzy logic, Motor, Neural networks, Nonlinear systems",
author = "Mok, {H. T.} and Chan, {C. W.}",
note = "Funding Information: This work was supported by the HKSAR RGC Grant (HKU 2050/02E) and The University of Hong Kong.",
year = "2005",
language = "English",
isbn = "008045108X",
series = "IFAC Proceedings Volumes (IFAC-PapersOnline)",
publisher = "IFAC Secretariat",
number = "1",
pages = "67--72",
booktitle = "Proceedings of the 16th IFAC World Congress, IFAC 2005",
edition = "1",
}