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C. D. Bocaniala and J. Sa da Costa, “Tuning the Parameters of a Fuzzy Classifier for Fault Diagnosis. Hill-Climbing vs Genetic Algorithms,” Proceedings of the 6th Portuguese Conference on Automatic Control, Faro, 7-9 June 2004, pp. 349-354.

has been cited by the following article:

• TITLE: A TSK-Type Recurrent Neuro-Fuzzy Systems for Fault Prognosis

  AUTHORS: Rafik Mahdaoui, Leila Hayet Mouss

  KEYWORDS: TSK Neuro-Fuzzy Systems; Faults Diagnosis; Fault Prognosis

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.5 No.7, June 15, 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 network 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.