WienerHammersteinConvolutionOfTwoFiltersParameters -...

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
% part a) in order to obtain a least squares solution obtain the matrix A %clear all N = 300; %# of training data r = 9; A = [-sim_out1(100+r:100+N-1) -sim_out1(100+r-1:100+N-2) -sim_out1(100+r-2:100+N- 3). .. -sim_out1(100+r-3:100+N-4) -sim_out1(100+r-4:100+N-5) -sim_out1(100+r-5:100+N- 6). .. -sim_out1(100+r-6:100+N-7) -sim_out1(100+r-7:100+N-8) -sim_out1(100+r-8:100+N-
Background image of page 1
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: 9). ..-sim_out1(100+r-9:100+N-10) sim_in(100+r+1:100+N) sim_in(100+r:100+N-1) ... sim_in(100+r-1:100+N-2) sim_in(100+r-2:100+N-3) sim_in(100+r-3:100+N-4) ]; b = sim_out1(100+r+1:100+N); prmtrs = A\b % A least squares solution is obtained....
View Full Document

This note was uploaded on 07/04/2011 for the course ECE 501 taught by Professor Deniz during the Spring '11 term at Istanbul Universitesi.

Ask a homework question - tutors are online