Description

The Least Squares Polynomial Fit block computes the coefficients of the nth order polynomial that best fits the input data in the least-squares sense, where you specify n in the Polynomial order parameter. A distinct set of n+1 coefficients is computed for each column of the M-by-N input, u.

For a given input column, the block computes the set of coefficients, c₁, c₂, ..., c_n+1, that minimizes the quantity

where u_i is the ith element in the input column, and

The values of the independent variable, x₁, x₂, ..., x_M, are specified as a length-M vector by the Control points parameter. The same M control points are used for all N polynomial fits, and can be equally or unequally spaced. The equivalent MATLAB^® code is shown below.

c = polyfit(x,u,n)						% Equivalent MATLAB code 

For convenience, the block treats length-M unoriented vector input as an M-by-1 matrix.

Each column of the (n+1)-by-N output matrix, c, represents a set of n+1 coefficients describing the best-fit polynomial for the corresponding column of the input. The coefficients in each column are arranged in order of descending exponents, c₁, c₂, ..., c_n+1.

Examples

In the ex_leastsquarespolyfit_ref model below, the Polynomial Evaluation block uses the second-order polynomial

to generate four values of dependent variable y from four values of independent variable u, received at the top port. The polynomial coefficients are supplied in the vector [-2 0 3] at the bottom port. Note that the coefficient of the first-order term is zero.

The Control points parameter of the Least Squares Polynomial Fit block is configured with the same four values of independent variable u that are used as input to the Polynomial Evaluation block, [1 2 3 4]. The Least Squares Polynomial Fit block uses these values together with the input values of dependent variable y to reconstruct the original polynomial coefficients.

Parameters

Supported Data Types

See Also