Soner Bekleric Title of Thesis: Nonlinear Prediction via Volterra Ser
Soner Bekleric Title of Thesis: Nonlinear Prediction via Volterra Ser
Soner Bekleric Title of Thesis: Nonlinear Prediction via Volterra Ser
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2.6. SUMMARY 25<br />
ward and forward predictions by minimizing the error between the predicted data<br />
and original data. Finally, I presented a least squares method that uses only the<br />
available data and avoid truncation effects by properly using all the available in-<br />
formation at the time <strong>of</strong> setting the system <strong>of</strong> linear prediction equations. Because<br />
<strong>of</strong> practical considerations, I will use the least squares approach presented in sec-<br />
tion 2.3.4 to solve for the coefficients <strong>of</strong> the nonlinear model that I will present in<br />
Chapter 3.