Re: Reed solomon
It is a signal modelling technique... and is used when we want to predict the future value of any signal...
Now to predict the future of a signal , we need :
*some information to tell the starting value or any reference point
*some information from its past...
*some information to tell how best is the current prediction..so that in future we can improve our prediction......i.e we have to compare the predicted signal with the actual signal.. When we do this comparison we get the error signal....This error signal is used to reduce the error in future prediction...
So, Autoregressive model is one such estimation technique. It predicts the output of a system based on the previous output.
Equation of AR model can be found in Digital Signal Processing by Proakis or any statistical modelling books.....If you see the equation of AR model it will have three parts:
1. Constant Part..
2. An error part..
3. Autoregressive summation part which represents the summation of previous inputs... The number of previous signal used in the summation represents the order of the system..If you take more number of previous signal then you can predict the future more accurately.. It means higher the order better the model...But higher order also means more computation......