MATLAB: Choosing not number elements from matrix to use in title figures and plots

plotsprintftitles

I want to use a list of names to put in titles of plots or figures.

This is the matrix and the code in my try.

sis = 1; % This defines which element of the sismo matrix element to display
sismo = {'1966 EW Lima'  ;  '1966 NS Lima'; ...
        '1970 EW Ancash'; '1970 NS Ancash';...
        '1974 EW Enero Lima'; '1974 NS Enero Lima';...
        '1974 EW Octubre Lima'; '1974 NS Octubre Lima';...
        '2001 EW Arequipa'; '2001 NS Arequipa';...
        '2007 EW Ica Callao'; '2007 NS Ica Callao'; ...
        '2007 EW Ica'; '2007 NS Ica'};
   sismo = sismo(sis);
figure(sprintf('Numero %s',sismo))

Best Answer

isletter gives the logical values where ever the letter is in your string. Use this is extract only letters and put this as title

clc
clear
sismo = {'1966 EW Lima'  ;  '1966 NS Lima'; ...
    '1970 EW Ancash'; '1970 NS Ancash';...
    '1974 EW Enero Lima'; '1974 NS Enero Lima';...
    '1974 EW Octubre Lima'; '1974 NS Octubre Lima';...
    '2001 EW Arequipa'; '2001 NS Arequipa';...
    '2007 EW Ica Callao'; '2007 NS Ica Callao'; ...
    '2007 EW Ica'; '2007 NS Ica'};
for i=1:length(sismo)
    subplot(3,5,i)
    plot(magic(2))
    %     title(char(regexp(sismo{i},'\d*','Match'))) %title as only numbers
    title(sismo{i}(isletter(sismo{i}))) %title as only letters
end

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MATLAB: Neural Network – Multi Step Ahead Prediction

Hi Jack,

When using narxnet, the network performs only a one-step ahead prediction after it has been trained. Therefore, you need to use closeloop to perform a multi-step-ahead prediction and turn the network into parallel configuration.

Take a look at this example for a multi-step-ahead prediction, N steps. This uses the dataset magdata.mat which is available in the Neural Network Toolbox. Also, some of the inputs will be used for performing the multi-step-ahead prediction, and results validated with the original data. I hope the comments help to understand.

Edited in September 2015 to simplify step 5

%% 1. Importing data

S = load('magdata');
X = con2seq(S.u);
T = con2seq(S.y);

%% 2. Data preparation

N = 300; % Multi-step ahead prediction
% Input and target series are divided in two groups of data:
% 1st group: used to train the network
inputSeries  = X(1:end-N);
targetSeries = T(1:end-N);
% 2nd group: this is the new data used for simulation. inputSeriesVal will 
% be used for predicting new targets. targetSeriesVal will be used for
% network validation after prediction
inputSeriesVal  = X(end-N+1:end);
targetSeriesVal = T(end-N+1:end); % This is generally not available

%% 3. Network Architecture

delay = 2;
neuronsHiddenLayer = 10;
% Network Creation
net = narxnet(1:delay,1:delay,neuronsHiddenLayer);

%% 4. Training the network

[Xs,Xi,Ai,Ts] = preparets(net,inputSeries,{},targetSeries); 
net = train(net,Xs,Ts,Xi,Ai);
view(net)
Y = net(Xs,Xi,Ai); 
% Performance for the series-parallel implementation, only 
% one-step-ahead prediction
perf = perform(net,Ts,Y);

%% 5. Multi-step ahead prediction

[Xs1,Xio,Aio] = preparets(net,inputSeries(1:end-delay),{},targetSeries(1:end-delay));
[Y1,Xfo,Afo] = net(Xs1,Xio,Aio);
[netc,Xic,Aic] = closeloop(net,Xfo,Afo);
[yPred,Xfc,Afc] = netc(inputSeriesVal,Xic,Aic);
multiStepPerformance = perform(net,yPred,targetSeriesVal);
view(netc)
figure;
plot([cell2mat(targetSeries),nan(1,N);
      nan(1,length(targetSeries)),cell2mat(yPred);
      nan(1,length(targetSeries)),cell2mat(targetSeriesVal)]')
legend('Original Targets','Network Predictions','Expected Outputs')

MATLAB: Forecasting by Neural Network

It is straightforward. The basic logic is

0. Standardize T using cell2mat and zscore

1. Minimize H subject to NMSEo <= 0.005

neto                   = narnet(FD,H);
[ Xo Xoi Aoi To ]      = preparets( neto,{},{}, T );
neto                   = configure( neto, Xoi, Aoi);
[ neto Yo Eo Xof Aof ] = train( neto, Xo, To, Xoi, Aoi );
% [ Yo Xof Aof ]         = net( Xo, Xoi ,Aoi );
% Eo                     = gsubtract(To,Yo);
NMSEo                  = mse(Eo)/mean(var(cell2mat(To}',1))

2. Close the loop on OL neto to obtain CL netc

[ netc Xci Aci ] = closeloop( neto, Xoi, Aoi );
[ Xc Xci Aci Tc ]= preparets( netc,{},{}, T );
[ Yc Xcf Acf ]   = netc( Xc, Xci ,Aci );
Ec               = gsubtract(Tc,Yc);
NMSEc            = mse(Ec)/mean(var(cell2mat(Tc}',1))

3. If NMSEc is not sufficiently small (<= 0.01, 0.05 ???) then train netc initialized with the final weights of neto

[ netc Yc Ec Xcf Acf ] = train( netc, Xc, Tc, Xci, Aci );
% [ Yc Xcf Acf ]         = netc( Xc, Xci ,Aci );
% Ec                     = gsubtract(Tc,Yc);
NMSEc                  = mse(Ec)/mean(var(cell2mat(Tc}',1))

4. Predict performance Np steps beyond the currently known data

[ Yc2 Xcf2 Acf2 ] = netc( cell(1,Np), Xcf ,Acf );

Hope this helps.

Thank you for formally accepting my answer

Greg

PS I'm sure this is covered in one of my previous posts in the NEWSGROUP or ANSWERS. Search using

 greg narnet closeloop

Best Answer

Related Solutions

MATLAB: Neural Network – Multi Step Ahead Prediction

MATLAB: Forecasting by Neural Network

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