Asking NN Backpropagation Using MATHEMATICA ver5.0
- To: mathgroup at smc.vnet.net
- Subject: [mg85042] Asking NN Backpropagation Using MATHEMATICA ver5.0
- From: Navri Navri Bintang <navri_bintang at yahoo.com>
- Date: Fri, 25 Jan 2008 05:02:34 -0500 (EST)
Hi,
I'm not very familiar with MATHEMATICA (still learning) and I have some problem with it.
I have 4 input data (symbol with M, R, P, T each have 40 values), and I want to fit these data with 2 output/target values (X & Y, each also have 40 values). And also I want to optimize these inputs toward maximum output/target data (Data shown below).
Here I?m trying to study it using neural network (NN). However, I?m facing difficulties with programming NN in MATHEMATICA (here I'm only have MATHEMATICA ver. 5.0).
I want to use backpropagation method to train my neural network (NN) problem.
I hope someone could help me to this matter.
And thank you very much
Rgd
IRVAN
Below are the data used (INPUT before & after normalized):
M = [5 15 5 15 5 15 5 15 0 20 10 10 10 10 10 10 10 10 10 10 5 15 5 15 5 15 5 15 0 20 10 10 10 10 10 10 10 10 10 10];
Data M after normalized [range 0,1]
M = [0.25 0.75 0.25 0.75 0.25 0.75 0.25 0.75 0 1 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.25 0.75 0.25 0.75 0.25 0.75 0.25 0.75 0 1 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5];
R = [1.5 1.5 3.5 3.5 1.5 1.5 3.5 3.5 2.5 2.5 0.5 4.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 1.5 1.5 3.5 3.5 1.5 1.5 3.5 3.5 2.5 2.5 0.5 4.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5];
Data R after normalized
R = [0.25 0.25 0.75 0.75 0.25 0.25 0.75 0.75 0.5 0.5 0 1 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.25 0.25 0.75 0.75 0.25 0.25 0.75 0.75 0.5 0.5 0 1 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5];
P = [12 12 12 12 24 24 24 24 18 18 18 18 6 30 18 18 18 18 18 18 12 12 12 12 24 24 24 24 18 18 18 18 6 30 18 18 18 18 18 18];
Data P after normalized
P = [0.25 0.25 0.25 0.25 0.75 0.75 0.75 0.75 0.5 0.5 0.5 0.5 0 1 0.5 0.5 0.5 0.5 0.5 0.5 0.25 0.25 0.25 0.25 0.75 0.75 0.75 0.75 0.5 0.5 0.5 0.5 0 1 0.5 0.5 0.5 0.5 0.5 0.5];
T = [300 300 300 300 300 300 300 300 300 300 300 300 300 300 300 300 300 300 300 300 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600];
Data T, I do not how to normalize it, because it is the type of material used
Here we have two outputs (target X, Y):
X = [50.96 95.75 46.33 83.39 74.13 86.48 80.30 89.57 35.52 97.29 58.68 61.77 77.22 106.56 95.75 94.20 95.75 95.75 95.75 94.20 57.14 84.94 52.51 74.13 75.67 91.11 84.94 78.76 47.87 86..48 69.49 49.42 63.32 95.75 86.48 88.03 88.03 86.48 88.03 88.03];
Data X after normalized
X = [0.22 0.85 0.15 0.67 0.54 0.72 0.63 0.76 0.00 0.87 0.33 0.37 0.59 1.00 0.85 0.83 0.85 0.85 0.85 0.83 0.30 0.70 0.24 0.54 0.57 0.78 0.70 0.61 0.17 0.72 0.48 0.20 0.39 0.85 0.72 0.74 0.74 0.72 0.74 0.74];
Y = [11.65 14.87 7.03 15.87 12.05 16.88 11.85 17.48 0 15.87 12.86 11.85 11.65 16.88 15.67 15.47 15.67 15.47 15.67 15.67 10.85 13.46 7.43 13.66 11.05 14.26 10.25 15.87 0 12.46 11.85 10.45 13.86 15.27 14.67 14.67 14.46 14.67 14.67 14.67];
Data Y after normalized
Y = [0.67 0.85 0.40 0.91 0.69 0.97 0.68 1.00 0.00 0.91 0.74 0.68 0.67 0.97 0.90 0.89 0.90 0.89 0.90 0.90 0.62 0.77 0.43 0.78 0.63 0.82 0.59 0.91 0.00 0.71 0.68 0.60 0.79 0.87 0.84 0.84 0.83 0.84 0.84 0.84];