Download presentation

Presentation is loading. Please wait.

1
NUMERICAL ERROR ENGR 351 Numerical Methods for Engineers Southern Illinois University Carbondale College of Engineering Dr. L.R. Chevalier

2
Copyright © 2003 by Lizette R. Chevalier Permission is granted to students at Southern Illinois University at Carbondale to make one copy of this material for use in the class ENGR 351, Numerical Methods for Engineers. No other permission is granted. All other rights are reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the copyright owner.

4
Objectives To understand error terms Become familiar with notation and techniques used in this course

5
Approximation and Errors Significant Figures 4 significant figures 1.845 0.01845 0.0001845 43,500 ? confidence 4.35 x 10 4 3 significant figures 4.350 x 10 4 4 significant figures 4.3500 x 10 4 5 significant figures

6
Accuracy and Precision Accuracy - how closely a computed or measured value agrees with the true value Precision - how closely individual computed or measured values agree with each other number of significant figures spread in repeated measurements or computations

7
increasing accuracy increasing precision Accuracy and Precision

8
Error Definitions Numerical error - use of approximations to represent exact mathematical operations and quantities true value = approximation + error error, t =true value - approximation subscript t represents the true error shortcoming....gives no sense of magnitude normalize by true value to get true relative error

9
Error definitions cont. True relative percent error

10
Example Consider a problem where the true answer is 7.91712. If you report the value as 7.92, answer the following questions. 1.How many significant figures did you use? 2.What is the true error? 3.What is the relative error?

11
Error definitions cont. May not know the true answer apriori

12
Error definitions cont. May not know the true answer apriori This leads us to develop an iterative approach of numerical methods

13
Error definitions cont. May not know the true answer apriori This leads us to develop an iterative approach of numerical methods

14
Error definitions cont. Usually not concerned with sign, but with tolerance Want to assure a result is correct to n significant figures

15
Error definitions cont. Usually not concerned with sign, but with tolerance Want to assure a result is correct to n significant figures

16
Example Consider a series expansion to estimate trigonometric functions Estimate sin / 2 to three significant figures

17
Error Definitions cont. Round off error - originate from the fact that computers retain only a fixed number of significant figures Truncation errors - errors that result from using an approximation in place of an exact mathematical procedure

18
Error Definitions cont. Round off error - originate from the fact that computers retain only a fixed number of significant figures Truncation errors - errors that result from using an approximation in place of an exact mathematical procedure To gain insight consider the mathematical formulation that is used widely in numerical methods - TAYLOR SERIES

19
TAYLOR SERIES Provides a means to predict a function value at one point in terms of the function value at and its derivatives at another point Zero order approximation

20
TAYLOR SERIES Provides a means to predict a function value at one point in terms of the function value at and its derivative at another point Zero order approximation This is good if the function is a constant.

21
Taylor Series Expansion First order approximation { slope multiplied by distance

22
Taylor Series Expansion First order approximation slope multiplied by distance Still a straight line but capable of predicting an increase or decrease - LINEAR

23
Taylor Series Expansion Second order approximation - captures some of the curvature

24
Taylor Series Expansion Second order approximation - captures some of the curvature

25
Taylor Series Expansion

27
Example Use zero through fourth order Taylor series expansion to approximate f(1) given f(0) = 1.2 (i.e. h = 1) Note: f(1) = 0.2

28
Solution n=0 f(1) = 1.2 = abs [(0.2 - 1.2)/0.2] x 100 = 500% n=1 f '(x) = -0.4x 3 - 0.45x 2 -x -0.25 f '(0) = -0.25 f(1) = 1.2 - 0.25h = 0.95 t =375%

29
n=2 f "=-1.2 x 2 - 0.9x -1 f "(0) = -1 f(1) = 0.45 t = 125% n=3 f "'=-2.4x - 0.9 f "'(0)=-0.9 f(1) = 0.3 t =50% Solution

30
n=4 f ""(0) = -2.4 f(1) = 0.2 EXACT Why does the fourth term give us an exact solution? The 5th derivative is zero In general, nth order polynomial, we get an exact solution with an nth order Taylor series Solution

32
Exam Question How many significant figures are in the following numbers? A. 3.215 B. 0.00083 C. 2.41 x 10 -3 D. 23,000,000 E. 2.3 x 10 7

33
Taylor Series Problem Use zero- through fourth-order Taylor series expansions to predict f(4) for f(x) = ln x using a base point at x = 2. Compute the percent relative error t for each approximation.

34
1. Determine the step size h = 4 - 2 = 2 2. Determine the analytical solution f(4) = ln(4) = 1.3863 3. Determine the derivatives for f(2)

Similar presentations

© 2019 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google