Recurrent Neural Networks for time
So far I haven’t been very satisfied with the actual performance of Neurogenesis when trying to evolve suitable recurrent neural networks. I suspect part of this is due to the fact that the current version doesn’t discriminate what kind of connections are allowed. Anything goes. You can have recurrent connections from any cell to any other cell including a loop back to the original cell itself. Also, you can have forward connections from the input layer to the output layer skipping the hidden layer of memory blocks.
I have hardly proven that in general this would be a bad idea but I do suspect that in most real world problems it is certainly less than optimal. Thus at least at some point in the future I’m certainly planning to implement a way to limit allowable connection destinations or at least make some destinations less likely than others.
One other thing that might improve performance would be the ability to make previously learned structure more rigid after each successive complexification step. Thus every time the program adds either memory blocks to existing networks (or memory cells to blocks) it would make it less probable that previously learned connections are changed in the next stages of evolution.
Subgrouped Real Time Recurrent Learning Neural Networks
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Criminal cheaters fuck themselves in the ass!2012-03-26 12:59:45 by HAHAHAHAHAHAHA!
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Continuous time delay stability of recurrent neural networks [Paperback](Chinese Edition)
Book (Northeastern University Press)