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
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
Related questions
Question
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."](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F21dfbca2-42a0-4f90-98e3-9d7bf80a59f9%2F88acf6c8-e3a0-4cc6-bd48-05c9a564a88e%2Fck00s2i_processed.jpeg&w=3840&q=75)
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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