- Description:
- Demonstrates the use of Matrix Transpose, Matrix Muliplication, and Matrix Inverse functions to apply least squares fitting to input data. Least squares fitting is the procedure for finding the best-fitting curve that minimizes the sum of the squares of the offsets (least square error) from a given set of data.
- Algorithm:
- The linear combination of parameters considered is as follows:
A * X = B
, where X
is the unknown value and can be estimated from A
& B
.
- The least squares estimate
X
is given by the following equation:
X = Inverse(AT * A) * AT * B
- Block Diagram:
-
- Variables Description:
A_f32
input matrix in the linear combination equation
B_f32
output matrix in the linear combination equation
X_f32
unknown matrix estimated using A_f32
& B_f32
matrices
- CMSIS DSP Software Library Functions Used:
- arm_mat_init_f32()
- arm_mat_trans_f32()
- arm_mat_mult_f32()
- arm_mat_inverse_f32()
Refer arm_matrix_example_f32.c