Although there are fancy ways to rank variables for neural networks, most are (in my opinion, too complicated to waste time over).
What I have found is that sufficiently good variable rankings can be obtained by using simple 1st and 2nd order polynomial models with MATLAB variable selection algorithms.
In doing so I first look at linear and linear with squares before considering cross products.
Those 3 results tend to yield good enough information for rejecting variables with low prediction ability.
Hope this helps.
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