Characterization of Functions Using Artificial Intelligence to Reproduce Complex Systems Behavior

Takagi Sugeno Kang Order 2 to Reproduce Cardiac PQRST Complex
  • Jesús Rodríguez-Flores
  • Víctor Herrera-PérezEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1194)


In the field of signal processing, for forecasting purposes, the characterization of functions is a key factor to be faced. In most of the cases, the characterization can be achieved by applying least square estimation (LSE) to polynomial functions; however, it is not fully in all cases. To contribute in this field, this article proposes a variant of artificial intelligence based on fuzzy characterization patterns initialized by Lagrange interpolators and trained with neuro-adaptive system. The aim is to minimize a cost function based on the absolute value between samples and their prediction. The proposal is applied to the characterization of cardiac PQRST complex as case study. The results show a satisfactory performance providing an error of around 1.42% compared to the normalized PQRST complex signal.


Characterization of functions Fuzzy system Cost function Cardiac PQRST complex Neuro-adaptive system Lagrange interpolator 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Facultad de Informática y ElectrónicaEscuela Superior Politécnica de ChimborazoRiobambaEcuador

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