this paper describes a modification to the quickprop method. i tried implementing it and it didn’t work, but i’m not really sure if i’m doing it right.

a quick test shows the initial value of the eta term should be around 1… but again not sure if i’m doing all the calculations correctly.

training of the cascade network still doesn’t seem to be right, even with the same general “circle” pattern it very rarely gets as close as my first try did. i might look into this paper by fahlman, which describes what seem to be similar to some of the problems i’m having. i think i will try to implement the “quickprop” algorithm described there.

next i will begin to re-code the cascade correlation network. it is based on a paper published by fahlman and lebiere called “the cascade-correlation learning architecture”. in the summer of 2006 i tried using this network style to match the spiral pattern presented in the article, but was not able to. the file fahlman-lebiere-90.pdf has been backed up on euclid.

this spiral was trained on a {2,12,12,12,1} ff net after 5000 iterations.