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M. Gonzalez-Olvera and Y. Tang, “A New Recurrent Neurofuzzy Network for Identification of Dynamic Systems,” Fuzzy Sets and Systems, Vol. 158, No. 10, 2007, pp. 1023-1035. doi:10.1016/j.fss.2006.10.002

has been cited by the following article:

• TITLE: Group Method of Data Handling for Modeling Magnetorheological Dampers

  AUTHORS: Khaled Assaleh, Tamer Shanableh, Yasmin Abu Kheil

  KEYWORDS: System Identification; Magneto-Rheological Dampers; Group Method of Data Handling; Polynomial Classifier

  JOURNAL NAME: Intelligent Control and Automation, Vol.4 No.1, February 18, 2013

  ABSTRACT: This paper proposes the use of Group Method of Data Handling (GMDH) technique for modeling Magneto-Rheological (MR) dampers in the context of system identification. GMDH is a multilayer network of quadratic neurons that offers an effective solution to modeling non-linear systems. As such, we propose the use of GMDH to approximate the forward and inverse dynamic behaviors of MR dampers. We also introduce two enhanced GMDH-based solutions. Firstly, a two-tier architecture is proposed whereby an enhanced GMD model is generated by the aid of a feedback scheme. Secondly, stepwise regression is used as a feature selection method prior to GMDH modeling. The proposed enhancements to GMDH are found to offer improved prediction results in terms of reducing the root-mean-squared error by around 40%.