{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

week08 - ECE 3150 Spring 2009 Week 8 Recitation There is...

Info iconThis preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon
ECE 3150 Spring 2009 Week 8 Recitation There is only Lab 2 report due. I got the most questions from least square fit and standard deviation. Introduce the fitting algorithm behind: Given a measurement of f i (x i ) , for example, this can be I D (V GS ) where you have measured I D for every given V GS under a given low V DS . For a range of VGS (corresponding say the 10 th to 20 th measured point), you would like to fit to f(x) = ax + b . We will define the standard deviation from the fitting function as: ( 29 ( 29 ( 29 11 20 10 2 = - = i i i i x f x f S The best fitting coefficient can be given as minimizing S with respect to the choice of a and b : 0 ; 0 ; 0 ; 0 2 2 2 2 = = b S a S b S a S This will give unique a and b for the fitting function f(x) = ax + b . Notice that we can fit to any function with arbitrary coefficients, since each coefficient will have its own equation to obtain its value. However, the closer f i (x i ) is to f(x) , the least error and uncertainty there will be.
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Image of page 2
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}