A Quantitative Approach to Universal Numerical Integrators (UNIS) with Computational Application

A Quantitative Approach to Universal Numerical Integrators (UNIS) with Computational Application

Abstract In this paper, we examine the mathematics that makes it possible to couple a conventional numerical integrator (e.g., Euler, Runge-Kutta, among others) with some universal approximator of functions (e.g., artificial neural networks, fuzzy inference systems, among others). These hybrid struc...

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Bibliographic Details
Main Authors: Paulo M. Tasinaffo, Luiz A. V. Dias, Adilson M. da Cunha
Format: Article
Language:English
Published: Springer Nature 2024-11-01
Series:Human-Centric Intelligent Systems
Subjects:
Universal Numerical Integrator (UNI)
Runge–Kutta Neural Network (RKNN)
Adams-Bashforth Neural Network (ABNN)
Adams-Moulton Neural Network (AMNN)
Predictive-Corrector Neural Network (PCNN)
Euler-Type Universal Numerical Integrator (E-TUNI)
Online Access:https://doi.org/10.1007/s44230-024-00084-0
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https://doi.org/10.1007/s44230-024-00084-0

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