i think i’ve gotten what a rough implementation of quickprop halfway working… one problem i was having previously was understanding the terms used in the paper. i am using α as the weight adjustment coefficient and λ as the momentum term, while the fahlman paper used ε as the weights coefficient and α as the momentum term. once i fixed a few problems coming from that misunderstanding the training seems to work sometimes. i am using the cascade net, but only looking at training with no hidden nodes being added with a simple problem that can be solved without hidden nodes. when it works the network error rapidly decreases, though the amount it decreases by each time through the training set jumps around a lot more than with the standard delta rule (which is expected). there are two different ways in which it can not work properly. first is when it gives a divide by zero ComplexInfinity error. this problem might be related to the solution of introducing the “maximum growth factor” as described on page 11 of the paper. second is when it continuously adjusts the weights by zero when there is still a large error. this is probably because at some point the weight change was zero, and using the quickprop formula with no modifications will lead to multiplication of all future weight adjustments by zero. a solution for this problem may also be discussed on page 11. modifications were made to the file cascnet10.nb, backed up on euclid.
