Linearity, Time Invariance, Causality, ETC.

In summary, the conversation discusses the linearity of an input/output system, with one participant expressing difficulty in proving it mathematically. It is mentioned that the definition of linearity differs between polynomial algebra and signal processing and that, in signal processing, a system is considered linear if it satisfies homogeneity and additivity. The example given does not satisfy these tests, leading to the conclusion that the system is not linear.
  • #1
dashkin111
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0

Homework Statement


Is the following input/output (x is input, y is output) system linear, time invariant, causal, and memoryless? Answer yes or no for each one.

Homework Equations



[tex]y(t)=2x(t)+3[/tex]

The Attempt at a Solution


My instinct tells me it's linear, but for some reason I have trouble showing it mathematically.It is linear if when you add scaled values of the input x(t), it equals the sum of the same scaled outputs. But it doesn't work out to be linear if I go by that definition
 
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  • #2
LOL. Of course you can't prove it. It’s a trick question. The word linear is used differently in polynomial algebra and in signal processing.

In algebra a linear equation in one that is of the form y(t) = ax(t)+b.
In signal processing it is one that satisfies homogeneity and additivity. You are obviously talking signal processing (I recognize the language) so forget what you have learned about in algebra and apply the two tests up above. Clue: does cy(t) c(ax(t) +b) = acx(t) + b?
 
  • #3
opps, I mean cy(t) = c(ax(t) +b) = acx(t) + b? Are they equal (of course not!)
 

1. What is linearity and why is it important in science?

Linearity is the property of a system where the output is directly proportional to the input. In other words, if the input is doubled, the output will also double. This is important in science because it allows us to make accurate predictions and understand the behavior of a system.

2. What does it mean for a system to be time-invariant?

A time-invariant system is one where the output is not affected by when the input was applied. This means that the system will behave the same way regardless of when the input was given. This is important in science because it allows us to study the behavior of a system over time and make predictions about its future behavior.

3. How do we determine causality in a scientific experiment?

Causality is the relationship between cause and effect. In a scientific experiment, we can determine causality by controlling all variables except for the one we are testing. This allows us to establish a cause and effect relationship between the independent and dependent variables.

4. What are some examples of non-causal relationships in science?

Non-causal relationships in science include correlation, where two variables may be related but do not cause each other, and spurious relationships, where two variables may appear to be related but are actually influenced by a third variable. These types of relationships can be misleading and require careful analysis to determine causality.

5. How do we test for linearity and time-invariance in a system?

To test for linearity in a system, we can perform tests such as the superposition principle, where the output is compared to the sum of the individual inputs. For time-invariance, we can perform time-shift tests, where the input is shifted in time and the output is compared. These tests can help us determine the linearity and time-invariance of a system.

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