Re: Asking NN Backpropagation Using MATHEMATICA ver5.0
- To: mathgroup at smc.vnet.net
- Subject: [mg85065] Re: Asking NN Backpropagation Using MATHEMATICA ver5.0
- From: Szabolcs Horvát <szhorvat at gmail.com>
- Date: Sun, 27 Jan 2008 05:39:58 -0500 (EST)
- Organization: University of Bergen
- References: <fnefrq$d9j$1@smc.vnet.net>
Navri Navri Bintang wrote: > 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. That would be difficult, because not only did you not ask a specific question, but you didn't even describe the problem you are trying to solve. If you want to use Mathematica to work with neural networks, why not search the wri library archive for this topic? http://library.wolfram.com/search/?query=neural+networks&collection=library&x=0&y=0 http://library.wolfram.com/search/?collection=library&query=backpropagation&x=0&y=0 There are lots of resources and pointers to books/packages (both free and non-free). Also, since this is a Mathematica newsgroup, it would help if at least you posted correct Mathematica syntax instead of another system's syntax. > 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]; >