MATLAB POLYSPACE RELEASE NOTES Manual de usuario descargar pdf (Pagina 189)

Modeling Data

In this section.. .

“Overview” on page 5-19

“Polynomial Regression” on page 5-19

“General Linear Regression” on page 5-20

Overview

Parametric models translate an understanding of data relationships into

analytic tools with predictive power. Polynomial and sinusoidal models are

simple choices for the up and down trends in the traffic data.

Note This section continues the data analysis from “Visualizing Data” on

page 5-14.

Polynomial Regression

Use the MATLAB polyfit function to estimate coefficients of polynomial

models, then use the MATLAB

polyval function to evaluate the model at

arbitrary values of the predictor.

The following code fits the traffic data at the third intersection with a

polynomial model of degree six:

c3 = count(:,3); % Data at intersection 3

tdata = (1:24)';

p_coeffs = polyfit(tdata,c3,6);

figure

plot(c3,'o-')

hold on

tfit = (1:0.01:24)';

yfit = polyval(p_coeffs,tfit);

plot(tfit,yfit,'r-','LineWidth',2)

legend('Data','Polynomial Fit','Location', 'NW' )

5-19

1 2 ... 184 185 186 187 188 189 190 191 192 193 194 ... 239 240

Sin comentarios

MATLAB POLYSPACE RELEASE NOTES Manual de usuario Pagina 189