# whether the system is linear or not for the given problem

Given the system: $$y(t)=x(t+1)+x(t−1)$$ is the system linear?

For a system to be a linear first it should satisfy zero input and zero output. How can we calculate output at 0 input if the system depends on future or past or both? Please explain with steps.

• Don't use that property of linear systems, since non-linear systems can behave like that too. Rather, use the superposition principle.
– MBaz
Commented Oct 16, 2017 at 14:27
• @MBaz The zero input, zero output condition (a special case of homogeneity) is one of the two things you need for superposition to hold.
– Peter K.
Commented Oct 16, 2017 at 14:38
• Control books require zero state and zero input linearity.
– user28715
Commented Oct 16, 2017 at 14:48
• @PeterK. I wanted to point out precisely that: it is a necessary, but not sufficient condition for linearity. But upon second reading, I see that I missed the "first" in "first it must satisfy...", so the OP probably already knows this.
– MBaz
Commented Oct 16, 2017 at 15:10

How can we calculate output at 0 input if the system depends on future or past or both?

Well, surely zero input just means; $$x(t) = 0~~~~\forall t$$ and the $\forall t$ means for all time: positive and negative.

Substituting that into the equation: $$y(t) = x(t+1) + x(t-1) = 0 + 0 = 0$$

So the system is homogeneous.

Well, as @Dilip points out, this isn't sufficient for homogeneity: we need the output to be $a y(t)$ for all inputs $a x(t)$. The case above just looks at $a=0$.

The next question is: does it satisfy additivity?

If $$x_{\tt total}(t) = x_1(t) + x_2(t)$$ then $$y_{\tt total}(t) = x_{\tt total}(t+1) + x_{\tt total}(t-1)\\ = x_1(t+1) + x_2(t+1) + x_1(t-1) + x_2(t-1)\\ = y_1(t) + y_2(t)$$ where $$y_1(t) = x_1(t+1) + x_1(t-1)\\ y_2(t) = x_2(t+1) + x_2(t-1)$$ so it satisfies additivity also.

• How can we calculate output at 0 input if the system depends on future or past or both? Video scan or review of recorded data? Commented Oct 17, 2017 at 17:58
• @rrogers : That means the system is not causal (if it depends on the future). That's OK, theoretically. It's also OK practically if your independent variable isn't "time". As you suggest, in image processing the independent variable is $x$ or $y$ and in recorded data, you can play it forwards or backwards. It's obviously not OK practically for real-time systems.
– Peter K.
Commented Oct 17, 2017 at 18:31
• Surely two different checks are unnecessary: just check whether the output $y_{\tt total}(t)$ corresponding to input $x_{\tt total}(t) = a x_1(t) + bx_2(t)$ equals $ay_1(t) + by_2(t)$ (which it does in this case) and you have proved linearity. Commented Oct 18, 2017 at 3:18
• @DilipSarwate The OP pulled out homogeneity separately, so I answered in kind. You are, as usual, correct. :-)
– Peter K.
Commented Oct 18, 2017 at 12:10
• Yes, but checking that zero input produces zero output is no guarantee of homogeneity. For example, a square-law device that maps $x(t)$ into $y(t) = x^2(t)$ is not homogeneous at all even though a zero input produces zero output. I wish you had not said $$y(t) = x(t+1) + x(t-1) = 0 + 0 = 0$$ So the system is homogeneous" because what has been checked does not prove homogeneity. Please consider an edit to point out to the OP that checking for zero output does not guarantee homogeneity. Commented Oct 18, 2017 at 20:28

This is a linear descriptor system. Assuming that +1, and -1 refers to discrete time quantities then it is also a discrete time system. In state space you can represent it via

\begin{align*} \begin{bmatrix}1 &0 &0\\0&1&0\\0&1&0\end{bmatrix} \begin{bmatrix}x(t)\\x(t+1)\\x(t+2)\end{bmatrix} &= \begin{bmatrix}0 &1 &0\\0&0&1\\0&0&1\end{bmatrix} \begin{bmatrix}x(t-1)\\x(t)\\x(t+1)\end{bmatrix} \\ y(t) &=\begin{bmatrix}1&0&1 \\\end{bmatrix}\begin{bmatrix}x(t-1)\\x(t)\\x(t+1)\end{bmatrix} \end{align*}

So this is basically \begin{align*} Ex &= Ax\\ y&=Cx \end{align*}