"Christian Vollmer" wrote in message <firstname.lastname@example.org>... > Hi, > > I'd like to train a layered recurrent network not only on a single sequence, but on multiple sequences. > Suppose I have a set of input sequences (for example i1=[1 2 3] and i2=[1 2 2]). There is also a set of corresponding output sequences (for example t1=[2 3 4] and t2=[2 2 3]). > One obvious way to train the network would be to concatenate all the input sequences to one big sequence i and all the target sequences to one big sequence t, then to convert them to cellarray via con2seq and then to train the network on that.
If there is no correlation between successive 3x1 sequences, then a dynamic network is inappropriate.
Instead, use a regression/curve-fitting net like FITNET or the obsolete NEWFIT.
The sizes of the input and target matrices should be