Characterization of Functions Using Artificial Intelligence to Reproduce Complex Systems Behavior: Takagi Sugeno Kang Order 2 to Reproduce Cardiac PQRST Complex

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 pbkp_rediction. 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.

Año de publicación:

2020

Keywords:

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

Fuente:

google

scopus