neural network deciding the next output

I'd like to make a piece of music and there are 16 sounds possible, but not every sound can follow the other (for example sound 'A' can be followed by sound 'B', 'D' and 'F' but not by 'c' and 'E' etc). I have substituted this as a vector with 16 state spaces (开发者_JS百科values between 0 and 9) and I'd like to use them as inputs for neural network. Let's say I get new values as random (from 0 to 9) for the 16 elements of my vector. Now what I'd like my network to decide is which is the best suited value from the 16 based on the value of the last output, because as I already mentioned, each element has a certain number of possible elements that can come afterwards. DO YOU THINK IT COULD BE SOLVED BY NEURAL NETWORK and WHAT KIND OF NN WOULD BE BEST SUITED. Does anyone have similar examples. THANKS!

Rather than using a neural network, you may just want to create a table of probabilities that mark transitions between your notes. Imagining you could only play A, C, and E:

    A     C     E
A   0.2   0.1   0.7
C   0.4   0.2   0.4
E   0.2   0.5   0.3

Using that table as an example, you could start on a random note (let's say a C). Then, using the table and starting from the row for 'C', we see that with a 0.4 probability we go to A, a 0.2 probability we go to C again, and a 0.4 probability we go to E. Then you just keep transitioning between your notes. A lot simpler than a neural network, and it may be closer to what you're looking for?