1. It doesn't matter how MATLAB initializes your net. You can always override the initialization using
help setwb
doc setwb
type setwb
2. Initializations are not random. They are pseudo-random. That means if you save the state of the rng, the initialization can be duplicated.
3. What different topologies are you considering? You do know that a single hidden layer MLP is a universal approximator? The only reason to use two or more hidden layers is to reduce the total number of unknown weights and thresholds. For a given training set size, the design is more stable and better able to generalize to nontraining data the fewer the number of unknown weights.
Hope this helps.
Thank you for formally accepting my answer
Greg
PS: Are you familiar with my double loop designs over number of hidden nodes (outer loop : Search h= Hmin:dH:Hmax) and pseudo-random weight initializations (inner loop: Search i = 1:Ntrials )?
