2nd Order difference eqn ZIR ZSR

In summary, the conversation discusses the use of the recursive method to obtain values for the impulse response and system response of a system described by a difference equation. The general and particular solutions for the system are also mentioned, with the particular solution representing the zero-input response (ZIR). It is also noted that the ZIR is the same as the homogeneous solution with given initial conditions. The conversation ends with a question about proving the relationship between ZSR and ZIR, and the role of convolution when the input is more than an impulse.
  • #1
trojsi
19
0

Homework Statement



I have the following difference equation;

[itex]y[n] -1.7y[n-1] -0.72y[n-2]=x[n][/itex]

with aux conditions; [itex]y[-1]=1, y[-2]=-2[/itex]

input; [itex]x[n] = (0.7)^{n}u[n][/itex]

I used the recursive method to get 5 consecutive values of the impulse response of the system and also 5 consecutive values of the system response.

I need to determine the ZIR response analytically and therefore I obtained the general solution and after, the particular solution below;[itex]Gen. sol = \frac{10}{9}p^{n} - \frac{5}{4}q^{n}[/itex]

[itex]part. sol = \frac{-216}{35}(\frac{10}{9})^{n} - \frac{164}{35}(\frac{5}{4})^{n}[/itex]

I assume that if in the process I put the zero as input and used the initial conditions, the particular solution would be the ZIR.

Homework Equations



I also need to prove that ZSR + ZIR = system output response. The system response values can be obtained from the recursive method. Is the impulse response(recursive method) the same as the ZSR of the system?

I tried to obtain the ZSR analytically but I did not manage to find a good source. I would appreciate any help. thanks
 
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  • #2
trojsi said:
I assume that if in the process I put the zero as input and used the initial conditions, the particular solution would be the ZIR.

The particular solution is the part of the solution due to the input *not* being zero.

When the input is zero, your system is:

[itex]y[n] -1.7y[n-1] -0.72y[n-2]=0[/itex]
with aux conditions; [itex]y[-1]=1, y[-2]=-2[/itex]

Which is actually your homogeneous solution with given initial conditions. That would be your ZIR.

I also need to prove that ZSR + ZIR = system output response. The system response values can be obtained from the recursive method. Is the impulse response(recursive method) the same as the ZSR of the system?

Yes it is, if the input is an impulse. If the input is more than an impulse, a convolution will have to be done.
 

Related to 2nd Order difference eqn ZIR ZSR

1. What is a 2nd order difference equation?

A 2nd order difference equation is a mathematical equation that describes the relationship between two consecutive terms of a sequence. It is a discrete-time equation that involves the current term, the previous term, and the term before that.

2. What is the meaning of ZIR and ZSR in a 2nd order difference equation?

ZIR stands for Zero Input Response and ZSR stands for Zero State Response. These terms refer to the two components of the overall response of a 2nd order difference equation. ZIR is the response of the system when there is no input, while ZSR is the response when there is no initial condition.

3. How is a 2nd order difference equation solved?

A 2nd order difference equation can be solved by using various methods, such as the forward substitution method, the backward substitution method, or the matrix method. These methods involve manipulating the equation to find the values of the unknown terms.

4. What is the significance of a 2nd order difference equation in science?

2nd order difference equations are used in various fields of science, such as physics, engineering, and economics, to model and analyze dynamic systems. They can help predict the behavior and trends of a system over time, and can also be used to design control systems.

5. Can a 2nd order difference equation be applied to real-life situations?

Yes, 2nd order difference equations can be applied to real-life situations, such as population growth, stock market fluctuations, and weather forecasting. By modeling these situations as 2nd order difference equations, scientists and researchers can gain insights into their behavior and make predictions about future outcomes.

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