MATLAB: Is possible solve the problem used ANN toolbox? (related future input)

annartificial neural networkforecastfuture inputnnoutputpredict

Hi All, I have some question.

First of all, Fore example, Database is like blelow.

——————

3 1 2 4 5 6 7

1 4 5 7 2 5 9

3 5 7 1 2 3 5

1 4 6 1 2 4 8

——————

1~6 column is Inputs.

7 column is Output.

In Fitting app ANN toolbox in MATLAB,

1~6 column – Inputs

7 column – Target

And then training, I get Output data used Target.

But, If I have 'future input'

How forecasting the Output used 'future input'?

future input is like below.

——————

1 4 5 6 2 1

4 5 1 2 6 7

6 1 5 4 2 1

——————

Best Answer

"I"nputs are I-dimensional column vectors and "O"utput targets are O-dimensional column vectors. Then

[ I N ] = size(inputmatrix)

[ O N ] = size(targetmatrix)

additional input vectors must be I-dimensional columns.

Hope this helps.

Thank you for formally accepting my answer

Greg

Related Solutions

MATLAB: Neural network with multiple inputs and single output – how to change processing functions and how to plot results

1. Type

 net = fitnet   % NO SEMICOLON

You will see that there are defaults for all parameters.

2. Type

 help fitnet   % and/or doc fitnet

You will see that, if you do not get a bad set of random weights you can design a net with just 3 commands

 >> [ x, t ]       = simplefit_dataset;
 >> [ net tr y e ] = train( fitnet, x, t ); % e = t - y
 >> Rsquare        = 1-mse( e )/var( t, 1 )

Rsquare =

      0.99998  % fraction of target variance that is modeled by the net

Therefore, all you have to worry about is

1. Initializing the RNG so that you can intelligently search for a good set of random initial weights.

2. Finding the smallest number of hidden nodes that will yield a satisfactory design.

3. Therefore, my serious designs use a double for loop. The outer loop h = Hmin:dH:Hmax over No. of hidden nodes and i = 1:Ntrials over sets of initial weights.

4. For posted examples, search using

 greg fitnet Ntrials

Hope this helps,

Thank you for formally accepting my answer

Greg

MATLAB: How to create Multiple Output Neural Networks

It is very simple:

For N I-dimensional "I"nputs paired with N O-dimensional "O"utput targets

 [ I N ] = size(inputmatrix)
 [ O N ] = size(targetmatrix)

Hope this helps.

Thank you for formally accepting my answer

Greg

Best Answer

Related Solutions

MATLAB: Neural network with multiple inputs and single output – how to change processing functions and how to plot results

MATLAB: How to create Multiple Output Neural Networks

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