Python ?üêç Curve Fit with Step Test Data.

Format:

Video

Series:

Academic Video Online

Language:

English

Subjects (All):

Curve fitting.

Mathematical optimization.

Physical Description:

1 online resource (15 minutes)

Other Title:

Python ?üêç Curve Fit with Step Test Data

Place of Publication:

[Place of publication not identified] : APMonitor.com, [date of publication not identified]

System Details:

video file

Summary:

The Scipy curve_fit function determines two unknown coefficients (dead-time and time constant) to minimize the difference between predicted and measured response values. Pandas imports the data and the dataframe header is diplayed with x.head(). Conditional statements are used to create a step function and curve_fit from Scipy.optimize finds the optimal parameter values that minimize a sum of squared error. A Matplotlib plot shows the function with optimal parameter values. An R-squared value is 0.999, showing excellent agreement between the predictions and measurements. Course Material: https://apmonitor.com/che263/index.php/Main/PythonSolveEquationsGithub Repository (see HW04.ipynb): https://github.com/APMonitor/learn_pythonSchedule: https://apmonitor.com/che263/index.php/Main/CourseScheduleSolution Videos: https://apmonitor.com/che263/index.php/Main/CourseHomework

Notes:

Title from resource description page (viewed November 16, 2021).

Part of the metadata in this record was created by AI.