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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
  • 44 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1194)

Abstract

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.

Keywords

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

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

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

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