Rete 5 con 20 neuroni SISTEMATA.m

xlsread('C:\Users\marco\Documents\Università\TESI\historyIndex.xls','A7:F1501')
Germ=ans(:,1)
It=ans(:,2)
Jap=ans(:,3)
NA=ans(:,4)
UK=ans(:,5)
GermRet=price2ret(Germ)
ItRet=price2ret(It)*100;
JapRet=price2ret(Jap)*100;
NARet=price2ret(NA)*100;
UKRet=price2ret(UK)*100;
% Solve an Autoregression Time-Series Problem with a NAR Neural Network
% Script generated by Neural Time Series app
% Created 13-Apr-2017 09:32:27
%
% This script assumes this variable is defined:
%
%   target - feedback time series
%test
target=[GermRet ItRet JapRet NARet UKRet]
%target = [Germ It Jap NA UK]
T = tonndata(target,false,false);

% Choose a Training Function
% For a list of all training functions type: help nntrain
% 'trainlm' is usually fastest.
% 'trainbr' takes longer but may be better for challenging problems.
% 'trainscg' uses less memory. Suitable in low memory situations.
trainFcn = 'trainlm';
% Create a Nonlinear Autoregressive Network
feedbackDelays = 1:2;
hiddenLayerSize = 10;
net = narnet(feedbackDelays,hiddenLayerSize,'open',trainFcn);

net.trainParam.max_fail=100000;
net.trainParam.num_epochs=1000;
net.trainParam.lr=0.01;
% Choose Feedback Pre/Post-Processing Functions
% Settings for feedback input are automatically applied to feedback output
% For a list of all processing functions type: help nnprocess

% Prepare the Data for Training and Simulation
% The function PREPARETS prepares timeseries data for a particular network,
% shifting time by the minimum amount to fill input states and layer
% states. Using PREPARETS allows you to keep your original time series data
% unchanged, while easily customizing it for networks with differing
% numbers of delays, with open loop or closed loop feedback modes.
[x,xi,ai,t] = preparets(net,{},{},T);

% Setup Division of Data for Training, Validation, Testing
% For a list of all data division functions type: help nndivide
net.divideFcn = 'divideblock';  
net.divideMode = 'time';  % Divide up every sample
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;

% Choose a Performance Function
% For a list of all performance functions type: help nnperformance
net.performFcn = 'mse';  % Mean Squared Error

% Train the Network
[net,tr] = adapt(net,x,t,xi,ai);

% Test the Network
y = net(x,xi,ai);
e = gsubtract(t,y);
performance = perform(net,t,y)
%Prediction
figure,plotresponse(t,y)
figure,ploterrhist(e)