What might be some potential methods to encode a 100-point signal (curve) for input to a Artificial Neural Network?  


  Example: we have a large number of 100-pt 'curves' ranging from flat-line to approximately a half-sine wave with a wide range of max
  amplitude.  The curves have some noise and occasional anomalous
  'bumps'.  These training set curves can be broken down into known
  positives or negatives for 'truthing'.


If I wanted to use an Artificial Neural Network to give a result for untrained data curves, what are some potential methods to encode the input data sets?  

Would transforming each of the 100 points in a curve to a [0.0000 .... 1.0000] range be useful (and thus we'd have 100 inputs to the ANN?  Or might another method produce better results?

NOTE: I understand that FFT and Power Spectrum analysis might be a completely different alternative here as well, but am focusing right now on a possible neural network method.

The answer is yes.  This statement often goes under the name "row rank equals column rank".  Knowing that, it is easy to search the internet for proofs.

Also any reputable linear algebra text should prove this: it is indeed a rather important result.

Finally, since you said that you had only a substitute lecturer, I won't castigate him, but this would be a distressing lacuna of knowledge for someone who is a regular linear algebra lecturer.  
