(→Linearity) |
(→Linearity) |
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Let x1[n] = <math>2n</math> | Let x1[n] = <math>2n</math> | ||
x2[n] = <math>n^2</math> | x2[n] = <math>n^2</math> | ||
| + | |||
| + | x1[n] + x2[n] = xtot[n] = <math>2n + n^2</math> ===> ytot[n] = 4* xtot[n] = <math>8n + 4n^2</math> | ||
| − | x1[n] | + | x1[n] ==> y1[n] = 8n x2[n] ==> y2[n] = <math>4n^2</math> |
| + | |||
| + | ytot = y1[n] + y2[n] = <math>8n + 4n^2</math> | ||
| + | |||
| + | <math>8n + 4n^2</math> = <math>8n + 4n^2</math> | ||
| + | |||
| + | Since the output of the two are the same, the system is linear. | ||
Revision as of 08:01, 11 September 2008
Linearity
Linearity- A system is linear if a constant that multiplies an input to a system is also present in the output. Adding any number of linear combinations of complex numbers and functions of time together does not affect the linearity of the system.
A*x1(t) + B*x2(t) => A*y1(t) + B*y2(t) .... extendable for any amount of complex numbers (A, B, C...) and functions (x1, x2, x3...)
Linear System: y[n] = 4 * x[n] Let x1[n] = $ 2n $ x2[n] = $ n^2 $ x1[n] + x2[n] = xtot[n] = $ 2n + n^2 $ ===> ytot[n] = 4* xtot[n] = $ 8n + 4n^2 $
x1[n] ==> y1[n] = 8n x2[n] ==> y2[n] = $ 4n^2 $
ytot = y1[n] + y2[n] = $ 8n + 4n^2 $
$ 8n + 4n^2 $ = $ 8n + 4n^2 $
Since the output of the two are the same, the system is linear.
