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Linear Least Squares to Solve Nonlinear Problems

#SoME1
26K views|2 years ago
💫 Short Summary

The video explains linear least squares for modeling data sets by minimizing errors through adjustments of parameters m and b in the line equation y=mx+b. It discusses derivatives with respect to parameters, matrix form equations for curve fitting, and modeling diode capacitance in circuit simulations. The technique ensures a smooth optimization process and accurate representation of data sets. The video concludes with a successful fit plotted alongside data and encourages viewers to try the process themselves.

✨ Highlights
📊 Transcript
Linear least squares method for modeling data sets by finding the best line representation.
00:50
Minimize errors by adjusting values of m and b in the line equation y=mx+b.
Squaring errors simplifies the problem and ensures a continuous derivative for smoother optimization.
Focuses on minimizing the sum of squared error terms for a more accurate representation of the data set.
Effectiveness of the method is illustrated by comparing minimization of numbers with their squared group.
Minimizing error squared by taking derivatives with respect to parameters m and b in linear least squares.
03:15
Equations are set up with unknowns m and b, which are linear in nature.
The fitting function can be expressed as a linear combination of basis functions, like polynomials or sine functions.
Differentiating with respect to c sub i simplifies the process, with only one non-zero term.
The segment delves into types of functions usable for linear least squares and hints at convenient ways to solve for unknown parameters.
Writing equations in matrix form for curve fitting applications.
06:10
Matrix form separates unknown parameters from basis functions for a cleaner and more organized representation.
Minimizing the error squared function by setting its derivative with respect to parameters equal to zero.
The matrix expression involves quadratic and linear terms that are differentiated accordingly.
Solving for parameters using the left pseudo inverse matrix product, particularly useful for curve fitting scenarios.
Modeling diode capacitance as a function of voltage in a circuit simulation.
10:55
The structure of the capacitance function and the unknown parameters involved are explained.
Through manipulation and logarithmic transformations, the parameters are separated from the independent variable, allowing for a matrix form solution.
Coefficients for the parameters are calculated, resulting in a successful fit plotted alongside the data.
The satisfaction of a close curve-data match is highlighted, encouraging viewers to try the process themselves.