Not exactly what i meant by pattern recognition, neural nets are fuzzy math for fuzzy patterns not set patterns think different handwriting input from different people and trying to come with the same output. I reread some of the other posts as well and it seems there is a bit of confusion as to how it works currently:
- Each neuron is connected to every neuron in the next layer
- Each neuron sends the same output to each neuron its connected to
- Each neuron calculates its output via an multiplication of an initial number and all its inputs and corrects that number via error detection.
Current Problem: The error detection that I think should be used makes all outputs eventually go to zero so my logic is wrong somewhere. The logic I use is:
error = expected_output - actual_output;
If I set all of the errors to this then the zero problem occurs. This is the major issue with its learning as far as I can tell.
edit: It can be used to learn an exact pattern but the biggest issue with that is that there is no gaurantee that it will be exact with any and every input at any given time due to the nature of the learning progression and curve.
Edited by margoose, 02 July 2011 - 10:51 PM.