Construction of Parallel Piecewise-Linear Interval Models for Nonlinear Dynamical Objects

Voliansky, Roman; Sadovoi, Oleksandr; Volianska, Nina; Sinkevych, Oleksiy

doi:10.1109/acitt.2019.8779945

Cited by 11 publications

(1 citation statement)

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“…to synthesize data-processing algorithms while detecting cyberattacks [46][47][48], to ensure high-level security against cyberattacks [49,50], and others; (8) Engineering and control: to describe nonlinear dynamic object behavior [51,52],…”

Section: Literature Review and Statement Of The Problemmentioning

confidence: 99%

A Method of Accuracy Increment Using Segmented Regression

et al. 2022

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The main purpose of mathematical model building while employing statistical data analysis is to obtain high accuracy of approximation within the range of observed data and sufficient predictive properties. One of the methods for creating mathematical models is to use the techniques of regression analysis. Regression analysis usually applies single polynomial functions of higher order as approximating curves. Such an approach provides high accuracy; however, in many cases, it does not match the geometrical structure of the observed data, which results in unsatisfactory predictive properties. Another approach is associated with the use of segmented functions as approximating curves. Such an approach has the problem of estimating the coordinates of the breakpoint between adjacent segments. This article proposes a new method for determining abscissas of the breakpoint for segmented regression, minimizing the standard deviation based on multidimensional paraboloid usage. The proposed method is explained by calculation examples obtained using statistical simulation and real data observation.

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