Nonlinear Curve-Fitting in the L 1 and L ∞ Norms

1980 ◽  
Vol 34 (150) ◽  
pp. 529 ◽  
Author(s):  
Richard I. Shrager ◽  
Edward Hill
Keyword(s):  
Curve Fitting ◽  
Nonlinear Curve Fitting ◽  
Fitting In ◽  
Nonlinear Curve

Kinetic study of the Jaffé reaction for quantifying creatinine in serum: 1. Alkalinity controlled with NaOH.

1987 ◽  
Vol 33 (2) ◽  
pp. 278-285 ◽  
Author(s):  
H L Pardue ◽  
B L Bacon ◽  
M G Nevius ◽  
J W Skoug
Keyword(s):  
Curve Fitting ◽  
Interactive Effects ◽  
Kinetic Behavior ◽  
Factorial Experimental Design ◽  
Fitting Method ◽  
Nonlinear Curve Fitting ◽  
Relative Standard ◽  
Standard Additions ◽  
Curve Fitting Method ◽  
Nonlinear Curve

Abstract We studied the kinetic behavior of the reaction of alkaline picrate and creatinine and evaluated a nonlinear curve-fitting method for quantifying creatinine in serum. Using a 3 X 3 factorial experimental design, we evaluated interactive effects among temperature and concentrations of creatinine, picrate, and NaOH. We found no evidence of interference by glucose or unconjugated bilirubin; the effects of the acetoacetate reaction, which is fast, are easily compensated by the curve-fitting method. The reaction with human serum albumin is very complex, but its effects are compensated by the curve-fitting method and by preparing standards containing 50 g of albumin per liter. Calibration plots are linear under a wide variety of conditions for both aqueous standards and standard additions of creatinine to pooled serum. Reproducibility studies with standards containing creatinine at 2, 10, and 20 mg/L yielded relative standard deviations (RSD) of 8.2, 2.5, and 1.3%, corresponding to absolute variations of 0.16, 0.25, and 0.26 mg/L. The average SD for 17 sera containing creatinine at 15-50 mg/L was 0.7 mg/L. The averages of ratios (as percent) of determined vs expected concentrations in 17 sera with added creatinine (7.27 mg/L) were 97.8% for aqueous standards, 99.9% for standards with added albumin.

Iterative method and FORTRAN code for nonlinear curve fitting

1988 ◽  
Vol 14 (4) ◽  
pp. 489-503 ◽  
Author(s):  
Marijke van Heeswijk ◽  
Christopher G. Fox
Keyword(s):  
Iterative Method ◽  
Curve Fitting ◽  
Fortran Code ◽  
Nonlinear Curve Fitting ◽  
Nonlinear Curve

Research of Method about Geomagnetic Sensor Data Processing by Optimal Idea

2011 ◽  
Vol 320 ◽  
pp. 647-650
Author(s):  
Chan Yuan Liu
Keyword(s):  
Data Processing ◽  
Curve Fitting ◽  
Least Square Method ◽  
Least Square ◽  
Sensor Data ◽  
Nonlinear Curve Fitting ◽  
Curve Equation ◽  
Nonlinear Curve ◽  
The Way ◽  
Circular Curve

A method of optimal idea, in the paper, is used to process geomagnetic sensor data. The curve fitting by use of the method is more convenient than least square method (LSM). It adapts especially to process nonlinear curve fitting. Circular curve equation is fitted depending on a set of geomagnetic sensor data. It proves that the way is convenient and feasible

MINSQ: Nonlinear Curve Fitting and Model Development.

1992 ◽  
Vol 67 (4) ◽  
pp. 570-571
Author(s):  
Nancy Mendell ◽  
Neal Oden
Keyword(s):  
Curve Fitting ◽  
Model Development ◽  
Nonlinear Curve Fitting ◽  
Nonlinear Curve

The Method of Least Squares

2011 ◽  
pp. 181-185 ◽  
Author(s):  
Bernd Jaeger
Keyword(s):  
Least Squares ◽  
Curve Fitting ◽  
Method Of Least Squares ◽  
Unknown Parameters ◽  
Nonlinear Curve Fitting ◽  
Nonlinear Curve ◽  
Fitting Problem

The method of least squares is a geometric principle of curve fitting. The unknown parameters of a function are calculated in such a way that the sum of squared differences between function values and measurements gets minimal. Examples are given for a linear and a nonlinear curve fitting problem. Consequences of model linearizations are explained.

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