Hello Rahul, As Arijit and I have discussed in this e-group some days ago, your case will result in normal equations Ra = -r where vector r is not constrained by matrix R. In fact, vector r is the cross-correlation between x and y. While Durbin's solution does not apply any more, Levinson's solution still does. Hope this will help. Regards, Miguel -- Miguel Arjona Ramez rahulparthasarthy wrote: > > Hello, > > I am trying to compute a solution using the least- square- error > principle to an overdetermined system of equations. > > The equation is as below: > > Error = summation (y[n] - summation (ai x[n-i] ) ).... in 'ai' > term `i' is subscript > > where `n' ranges from 1 to 300 and `i' ranges from 1 to 18. The > outside summation is for `n' (1,2,3,4,?299,300) whereas the inside > summation is for 'i' (1,2,3,4?18) > > I am interested in calculating `ai ` ( i is subscript) where 'ai' > represents Linear Prediction Coefficients. I could have used the > Levinson-Durbin algorithm but in this case, two different signals are > in consideration, namely, y[n] and x[n], and hence > the `autocorrelation' method cannot be used. I have y[n] and x[n] > available but not able to evaluate that expression. > > I would be obliged if anybody could help me in solving/evaluating > the above equation so that I can find ai's (LP coefficients). Is it > possible to solve using Maple, Matlab, Mathematica etc? I am > interested in writing a C program for the above expression. > > Regards, > Rahul Parthasarthy > > _____________________________________ > Note: If you do a simple "reply" with your email client, only the author of this message will receive your answer. You need to do a "reply all" if you want your answer to be distributed to the entire group. |