MATLAB: Artificial neural network (ANN), trainlm, traingda trainbfg and trainrp

artificial neural network (ann)traingda trainbfg and trainrptrainlm

i use ANN in my study ihave I fund good regression but the problem I faced is the MAE MSE RMSE are not good there is very big different after I compere the actual and the predicted data

I use data it content three types of asphalt those come from three types of petrol middle east, Russian and Venezuelan and 3 angles (3,5and 7) degree I arraignment the data that was like this All data for phase angle complex modules 1008 sample for modify test every test All data for phase angle complex modules 322 sample for control test every test the first three Columns Are the input( the first one is freq second one temperature third one the angel of twist fourth one the types of petrol ) I defined the types by number 1,2 and 3 Number 1 Middle East Number 2 Russian Number 3 Venezuelan last one is the output(after sort it in Microsoft Excel) I use Microsoft Excel to find the errors by Excel equations my question is winch type of algorithm i must use in my work

Best Answer

The current NN function to use for regression is

 FITNET (a special case of FEEDFORWARDNET)

it was created to replace

 NEWFIT ( a special case of NEWFF)

For details use the help and doc commands. Then, you can look at some of my posts in both the NEWSGROUP and ANSWERS. For example, search using

 greg fitnet

Hope this helps.

Thank you for formally accepting my answer

Greg

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The training and test indices are given in tr. Type, without semicolon

 tr = tr

to see what info is in tr.

Hope this helps.

Thank you for formally accepting my answer

Greg

MATLAB: Artificial neural network (ANN), trainlm, traingda trainbfg and trainrp

Here is a simple comparison of the 4 training functions on the dataset used in the fitnet and feedforwardnet documentation.

 close all, clear all, clc
 [ x, t ] = simplefit_dataset;
 vart = var(t,0)  % 8.4274  1-dim Reference MSE 
 figure, subplot(211), hold on 
 plot(x), plot(t,'r')
 subplot(212), plot( x,t ,'r')

% t vs x shows 4 local extrema. Therefore choose

 H=4
 net1 = fitnet(H); net1.trainFcn = 'trainlm';
 net2 = fitnet(H); net2.trainFcn = 'trainbfg';
 net3 = fitnet(H); net3.trainFcn = 'trainrp';
 net4 = fitnet(H); net4.trainFcn = 'traingda';
 rng(0), [ net1 tr1 y1 e1 ] = train(net1,x,t); 
 rng(0), [ net2 tr2 y2 e2 ] = train(net2,x,t); 
 rng(0), [ net3 tr3 y3 e3 ] = train(net3,x,t); 
 rng(0), [ net4 tr4 y4 e4 ] = train(net4,x,t);   
 NMSE1 = mse(e1)/vart % 0.00033605
 NMSE2 = mse(e2)/vart % 0.02683
 NMSE3 = mse(e3)/vart % 0.096204
 NMSE4 = mse(e4)/vart % 0.15838

Although it is a nice example, it doesn't prove much because, in addition to default parameter values (e.g, mu and min_grad) the rankings depend on

 1. The underlying function t = f(x)
 2. Random datadivision
 3. Random initial weights

For a serious comparision with nontrivial data, you would need at least tens of repetitions.

Hope this helps.

Greg

Best Answer

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MATLAB: Artificial neural network (ANN), trainlm, traingda trainbfg and trainrp

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