You also can use the Curve Fitting Express VI in LabVIEW to develop a curve fitting application. Therefore, you can adjust the weight of the outliers, even set the weight to 0, to eliminate the negative influence. If you calculate the outliers at the same weight as the data samples, you risk a negative effect on the fitting result. In some cases, outliers exist in the data set due to external factors such as noise. The Weight input default is 1, which means all data samples have the same influence on the fitting result. Therefore, you first must choose an appropriate fitting model based on the data distribution shape, and then judge if the model is suitable according to the result.Įvery fitting model VI in LabVIEW has a Weight input. An improper choice, for example, using a linear model to fit logarithmic data, leads to an incorrect fitting result or a result that inaccurately determines the characteristics of the data set. Curve Fitting Models in LabVIEWīefore fitting the data set, you must decide which fitting model to use. The following graphs show the different types of fitting models you can create with LabVIEW.įigure 1. Refer to the LabVIEW Help for information about using these VIs. These VIs create different types of curve fitting models for the data set. In LabVIEW, you can use the following VIs to calculate the curve fitting function. In geometry, curve fitting is a curve y= f( x) that fits the data ( x i, y i) where i=0, 1, 2,…, n–1. The residual is the distance between the data samples and f( x). The function f( x) minimizes the residual under the weight W. The purpose of curve fitting is to find a function f( x) in a function class Φ for the data ( x i, y i) where i=0, 1, 2,…, n–1. This document describes the different curve fitting models, methods, and the LabVIEW VIs you can use to perform curve fitting. Estimate the variable value outside the data sample range.Estimate the variable value between data samples.Find the mathematical relationship or function among variables and use that function to perform further data processing, such as error compensation, velocity and acceleration calculation, and so on.You can use curve fitting to perform the following tasks: One way to find the mathematical relationship is curve fitting, which defines an appropriate curve to fit the observed values and uses a curve function to analyze the relationship between the variables. However, the methods of processing and extracting useful information from the acquired data become a challenge.ĭuring the test and measurement process, you often see a mathematical relationship between observed values and independent variables, such as the relationship between temperature measurements, an observable value, and measurement error, an independent variable that results from an inaccurate measuring device. As the usage of digital measurement instruments during the test and measurement process increases, acquiring large quantities of data becomes easier.
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