@article {3392,
	title = {A TSK-Type Recurrent Neuro-Fuzzy Systems for Fault Prognosis JSEA, ISSN Print: 1945-3116},
	journal = {Journal of Software Engineering and Applications},
	volume = {Vol 5},
	number = {N{\textdegree}7},
	year = {2012},
	abstract = {As a result from the demanding of process safety, reliability and environmental constraints, a called of fault detection and diagnosis system become more and more important. In this article some basic aspects of TSK (Takigi Sugeno Kang) neuro-fuzzy techniques for the prognosis and diagnosis of manufacturing systems are presented. In particular, a neuro-fuzzy model that can be used for the identification and the simulation of faults prognosis models is described. The presented model is motivated by a cooperative neuro-fuzzy approach based on a vectorized recurrent neural net-work architecture. The neuro-fuzzy architecture maps the residuals into two classes: a one of fixed direction residuals and another one of faults belonging to rotary kiln},
	url = {https://www.researchgate.net/publication/265800845_A_TSK-Type_Recurrent_Neuro-Fuzzy_Systems_for_Fault_Prognosis},
	author = {Rafik Mahdaoui and Leila Hayet Mouss}
}
