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# MatLab – Palm Chapter 5 Curve Fitting - PowerPoint PPT Presentation

MatLab – Palm Chapter 5 Curve Fitting. Class 14.1 Palm Chapter: 5.5-5.7. RAT 14.1. As in INDIVIDUAL you have 1 minute to answer the following question and another 30 seconds to turn it in. Ready? When (day and time) and where is Exam #3? The answer is: Thursday at 6:30 pm,

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### MatLab – Palm Chapter 5Curve Fitting

Class 14.1 Palm Chapter: 5.5-5.7

ENGR 111A - Fall 2004

• As in INDIVIDUAL you have 1 minute to answer the following question and another 30 seconds to turn it in. Ready?

• When (day and time) and where is Exam #3?

• The answer is: Thursday at 6:30 pm,

Bright 124

• Do we have any schedule problems?

ENGR 111A - Fall 2004

• Students should be able to:

• Use the Function Discovery (i.e., curve fitting) Techniques

• Use Regression Analysis

ENGR 111A - Fall 2004

• Engineers use a few standard functions to represent physical conditions for design purposes. They are:

• Linear: y(x) = mx + b

• Power: y(x) = bxm

• Exponential: y(x) = bemx (Naperian)

y(x) = b(10)mx (Briggsian)

• The corresponding plot types are explained at the top of p. 299.

ENGR 111A - Fall 2004

• Examine data and theory near the origin; look for zeros and ones for a hint as to type.

• Plot using rectilinear scales; if it is a straight line, it’s linear. Otherwise:

• y(0) = 0 try power function

• Otherwise, try exponential function

• If power function, log-log is a straight line.

• If exponential, semi-log is a straight line.

ENGR 111A - Fall 2004

• polyfit( ) will provide the slope and y-intercept of the BEST fit line if a line function is specified.

• Linear: polyfit(x, y, 1)

• Power: polyfit(log10(x),log10(y),1)

• Exponential: polyfit(x,log10(y),1); Briggsian

polyfit(x,log(y),1); Naperian

Note: the use of log10( ) or log( ) to transform the data to a linear dataset.

ENGR 111A - Fall 2004

Example 5.5-1:Cantilever Beam Deflection

• First, input the data table on page 304.

• Next, plot deflection versus force (use data symbols or a line?)

• Then, add axes and labels.

• Use polyfit() to fit a line.

• Hold the plot and add the fitted line to your graph.

ENGR 111A - Fall 2004

ENGR 111A - Fall 2004

ENGR 111A - Fall 2004

Exponential function: y = bemx

Take the Naperian logarithm of both sides:

ln(y) = ln(bemx)

ln(y) = ln(b) + mx(ln(e))

ln(y) = ln(b) + mx

Thus, if the x value is plotted on a linear scale and the y value on a log scale, it is a straight line with a slope of m and y-intercept of ln(b).

ENGR 111A - Fall 2004

Exponential function: y = b10mx

Take the Briggsian logarithm of both sides:

log(y) = log(b10mx)

log(y) = log(b) + mx(log(10))

log(y) = log(b) + mx

Thus, if the x value is plotted on a linear scale and the y value on a log scale, it is a straight line. (Same as Naperian.)

ENGR 111A - Fall 2004

Power function: y = bxm

Take the Briggsian logarithm of both sides:

log(y) = log(bxm)

log(y) = log(b) + log(xm)

log(y) = log(b) + mlog(x)

Thus, if the x and y values are plotted on a on a log scale, it is a straight line. (Same can be done with Naperian log.)

ENGR 111A - Fall 2004

Given:

x=[1 2 3 4 5 6 7 8 9 10];

y1=[3 5 7 8 10 14 15 17 20 21];

y2=[3 8 16 24 34 44 56 68 81 95];

y3=[8 11 15 20 27 36 49 66 89 121];

• Use MATLAB to plot x vs each of the y data sets.

• Chose the best coordinate system for the data.

• Be ready to explain why the system you chose is the best one.

ENGR 111A - Fall 2004

ENGR 111A - Fall 2004

• What value does the first tick mark after 100 represent? What about the tick mark after 101 or 102?

• Where is zero on a log scale? Or -25?

• See pages 282 and 284 of Palm for more special characteristics of logarithmic plots.

ENGR 111A - Fall 2004

• Linear: pl = polyfit(x, y, 1)

• m = pl(1); b = pl(2) of BEST FIT line.

• Power: pp = polyfit(log10(x),log10(y),1)

• m = pp(1); b = 10^pp(2) of BEST FIT line.

• Exponential: pe = polyfit(x,log10(y),1)

• m = pe(1); b = 10^pe(2), best fit line using Briggsian base.

OR pe = polyfit(x,log(y),1)

• m = pe(1); b = exp(pe(2)), best fit line using Naperian base.

ENGR 111A - Fall 2004

• Determine the equation of the best-fit line for each of the data sets in In-class Assignment 14.1.1

• Hint: use the result from ICA 14.1.1 and the polyfit( ) function in MatLab.

• Plot the fitted lines in the figure.

ENGR 111A - Fall 2004

ENGR 111A - Fall 2004

• Involves a dependent variable (y) as a function of an independent variable (x), generally: y = mx + b

• We use a “best fit” line through the data as an approximation to establish the values of: m = slope and b = y-axis intercept.

• We either “eye ball” a line with a straight-edge or use the method of least squares to find these values.

ENGR 111A - Fall 2004

• Use Linear Regression unless you know that the data follows a different pattern: like n-degree polynomials, multiple linear, log-log, etc.

• We will explore 1st (linear), … 4th order fits.

• Cubic splines (piecewise, cubic) are a recently developed mathematical technique that closely follows the “ship’s” curves and analogue spline curves used in design offices for centuries for airplane and ship building.

• Curve fitting is a common practice used my engineers.

ENGR 111A - Fall 2004

• Solve problem T5.6-1 on page 318.

• Notice that the fit looks better the higher the order – you can make it go through the points.

• Use your fitted curves to estimate y at x = 10. Which order polynomial do you trust more out at x = 10? Why?

ENGR 111A - Fall 2004

ENGR 111A - Fall 2004

ENGR 111A - Fall 2004

• Prepare for Exam #3.

• Group Projects are due at Exam #3(parts 1 through 3 required; parts 4 and 5 as extra credit)

ENGR 111A - Fall 2004