LINEARITY
Linearity, in my definition, means that superposition always works. In other words, summation of inputs yield summation of outputs.
Example of Linearity and its proof
$ \,y(t)=x(2t)\, $
Proof:
$ x1(t) \to System \to y1(t)=x1(2t) \to Scalar multiplication(*a) \to ax1(2t) $
$ x2(t) \to System \to y2(t)=x2(2t) \to Scalar multiplication(*b) \to bx2(2t) $
$ ax1(2t) and bx2(2t) \to SUM \to ax1(2t)+bx2(2t) $
$ x1(t) \to Scalar multiplication(*a) \to ax1(t) $
$ x2(t) \to Scalar multiplication(*b) \to bx2(t) $
$ ax1(t) and bx2(t) \to SUM \to \to System \to ax1(2t)+bx2(2t) $
Those two yielded the same outputs thus it is linear.
Example of non-linearity and its proof
$ \,y(t)=e^{x(t)}\, $
Proof:
$ x1(t) \to System \to y1(t)=e^{x1(t)} \to Scalar multiplication(*a) \to ae^{x1(t)} $
$ x2(t) \to System \to y2(t)=e^{x2(t)}\to Scalar multiplication(*b) \to be^{x2(t)} $
$ ae^{x1(t)} and be^{x2(t)} \to SUM \to ae^{x1(t)}+be^{x2(t)} $
$ x1(t) \to Scalar multiplication(*a) \to ax1(t) $
$ x2(t) \to Scalar multiplication(*b) \to bx2(t) $
$ ax1(t) and bx2(t) \to SUM \to \to System \to e^{ax1(2t)+bx2(2t)}=e^{ax1(2t)}e^{bx2(2t)} $
Those two yielded the same outputs thus it is linear.
