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x=(0:0.1:2.5)'; y = erf(x); - in MATLAB. Assume that the output y(t) can be approximated by a sixth – th degree polynomial in terms of x(t) (including a constant bias term, so seven pa- rameters in total): _y(t) = 0₁ +0₂x(t) + 03x² (1) + 04x³ (1) + 05xª (1) + 06x³ (1) + 07xº(t) Solve for the coefficients 0₁, i = 1,2,3,4,5,6,7 using batch least squares. Com- pare the result with the MATI AR function "polyfit"

x=(0:0.1:2.5)'; y = erf(x); - in MATLAB. Assume that the output y(t) can be approximated by a sixth – th degree polynomial in terms of x(t) (including a constant bias term, so seven pa- rameters in total): _y(t) = 0₁ +0₂x(t) + 03x² (1) + 04x³ (1) + 05xª (1) + 06x³ (1) + 07xº(t) Solve for the coefficients 0₁, i = 1,2,3,4,5,6,7 using batch least squares. Com- pare the result with the MATI AR function "polyfit"

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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y(t) = 01 + 02x(t) + O3x² (t) + O4x*(t) + 0sx* (t) + O6x*(t) +0,x°(t) Solve. M

 

1 Gen ate pairs of data (xi, Yi) using = (0 : 0.1 : 2.5)';y=erf(x); in MATLAB. Assume that the output y(t) can be approximated by a sixth - th degree polynomial in terms of x(t) (including a constant bias term, so seven pa- rameters in total): _y(t) = 0₁ + 0₂x(1) + 03x² (1) + 04x³ (1) + 05xª (1) + 0x³ (1) + 07xº (1) Solve for the coefficients 0₁, i = 1,2,3,4,5,6,7 using batch least squares. Com- pare the result with the MATLAR function "polyfit."
Transcribed Image Text:1 Gen ate pairs of data (xi, Yi) using = (0 : 0.1 : 2.5)';y=erf(x); in MATLAB. Assume that the output y(t) can be approximated by a sixth - th degree polynomial in terms of x(t) (including a constant bias term, so seven pa- rameters in total): _y(t) = 0₁ + 0₂x(1) + 03x² (1) + 04x³ (1) + 05xª (1) + 0x³ (1) + 07xº (1) Solve for the coefficients 0₁, i = 1,2,3,4,5,6,7 using batch least squares. Com- pare the result with the MATLAR function "polyfit."
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