@article{MAKHILLAJIT2006575165,
    title = {Neuro Fuzzy Methods for Fault Diagnosis of Nonlinear Systems},
    journal = {Asian Journal of Information Technology},
    volume = {5},
    number = {7},
    pages = {750-760},
    year = {2006},
    issn = {1682-3915},
    doi = {ajit.2006.750.760},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2006.750.760},
    author = {Mehennaoui, L.,N. Debbache and},
    keywords = {Fuzzy identification,neural identification,fault diagnosis,neuro-fuzzy scheme},
    abstract = {The study presents a Fault Detection and Isolation (FDI) scheme with a particular emphasis placed on sensor fault diagnosis in nonlinear dynamic systems. The non-analytical FDI scheme is based on a two-step procedure. Two methods are proposed for the first step, called residual generation, one use fuzzy sets and the second neuronal network. A fuzzy neural network performs the second step, called residual evaluation. Some simulation results are given for efficiency assessment of this fault diagnosis approach.}
    }