scale range for neural network

Dear All;

I have in neural network, : My input range is starting with small value , then it become big ( 1000 times of the small values) which a lot of data accumulate in small range and make conflict between detail in this range , how can we solve it . Also, if I have data range overlap at the boundary which may lead to misinterpretation, what is the best way to overcome this issue.

Regards;

Answer 1

[Greg Heath]

I don't have any specific code. I would experiment with the following.

1. Worry about the gating later.

2. First, determine, by trial and error, subsets of effective ranges for inputs of fitnet. This will not be an easy task. It is quite possible that nonlinear input transformations (e.g., logs or powers ) may be be helpful.

3. Since fitnet defaults to mapminmax transformations of inputs and outputs before other calculations, what has to be determined is how to choose the different ranges of inputs that will be transformed to [-1,1].

4. If there are c input range categories the gating net targets should be {0,1} c-dimensional unit vectors. The transformations between the vectors and category indices are

 targets = ind2vec(indices)

 indices = vec2ind(targets)

Hope this helps.

Thank you for formally accepting my answer

Greg

Answer 2

[Greg Heath]

This is easily solved by using a gating net that sends the input to a following net designed for a specific range of inputs.

Hope this helps

Thank you for formally accepting my answer

Greg

[mustafa alnasser]

Thank you for answer but how to use gating in neural network Regards