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At 05.58 17/02/03 -0500, you wrote:
>I think it depends on how weights are adjusted during training.
you're right, and it depends from network topolgy. For example, time lagged
recurrent network use non-linear recurrent adaption of weights, and so
training can get caught in local minima.
In fact, I always train this kind of network multiple times, because it's
not unusal to get caught in local minima: for example, the first training
process may use N1 epochs and the error is E1, while the second train may
use N2>N1 with a better E2<E1.
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