Spatial crosstalk and modular NN architechture

[Rajiv Khosla]

Dear Connectionists,

            Can anyone enlighten me on the following.

         I have to model a problem with 28 discrete inputs(1's and 0's) and
26 discrete outputs. Infact, these 26 discrete outputs can be represented by
5 normalized continous outputs also.

               Now, I have no problem modelling it as a 28-11-5  network using
Scott Fahlman's quickprop . However, I get into all sorts of problems when I
have to model 28-?-26 network(? stands for any no. of hid. units. I tried upto
104). Sometime back, I read a paper on modular NN architechtures which suggested
that because of spatial crosstalk  one should have dedicated or independent links between hidden units and each output unit. This would result in faster
training and better generalization. I tried this architechture by making suitable changes in the quickprop algorithm but to no avail. There is no improvement
over the standard architechture vis-a-vis training. Infact, things seemed to get
slightly worse. I tried with 2,3,4 sets(that is, in all 52,78,104 hid. units 
resp.) of hid. units per output unit. I gave up after about 5000 epochs as I
couldn't see any significant improvement in the total error. 

            Has anyone used the modular architechture in a similar situation
with large number of output  nodes with positive results? Am I doing something
wrong? Is there any other solution  except making the outputs continous and
reducing the number of output nodes?

               I have only recently started reading this group. So, Pl. excuse
the naiveity of the questions if any.

  Please e-mail  your replies to khosla at latcs1.lat.oz.au

                         Thanks in advance,
                                                    Rajiv