Set up a small running table and evolve the difference equation \(y_n = x_n - 2x_{n-1} + 3y_{n-1}\) one step at a time, treating the record as starting at rest (\(x_{-1}=y_{-1}=0\)):
| n | x_n | x_(n-1) | y_(n-1) | y_n = x_n - 2x_(n-1) + 3y_(n-1) |
|---|---|---|---|---|
| 0 | 1.2 | 0 | 0 | 1.2 |
| 1 | -0.8 | 1.2 | 1.2 | -0.8 - 2.4 + 3.6 = 0.4 |
| 2 | 2.3 | -0.8 | 0.4 | 2.3 + 1.6 + 1.2 = 5.1 |
Each row only needs the previous input and the previous output, which is the defining feature of a first-order recursive (IIR) filter - the feedback term \(3y_{n-1}\) gives the filter memory, so its output at any sample depends on the entire past history, not just the current input.
Reading off the last row, \(y_2 = 5.1\).
\(\boxed{y_2 = 5.1}\)
