I have two variables, one of them consists a series (x) from 1 to 365 (days) and the other consists real numbers (y). I use nonlinear autoregressive neural network with external input (narx) and I would like to predict the y value in the future, so if the x is bigger then 365. I deployed the neural network, but I don't know how to use it.
I tried in this way, but the return values has no connections with the real data. How should I use this function?
X = [0 0];X = num2cell(X);X = transpose(X);for i = 1:365 Xi = [i 0]; Xi = num2cell(Xi); Xi = transpose(Xi); [Y,Xf,Af] = ann_test2_generated(X, Xi); Y endHere is my deployed function:
function [Y,Xf,Af] = myNeuralNetworkFunction(X,Xi,~)%MYNEURALNETWORKFUNCTION neural network simulation function.
%
% Generated by Neural Network Toolbox function genFunction, 29-May-2015 12:42:30.
%% [Y,Xf,Af] = myNeuralNetworkFunction(X,Xi,~) takes these arguments:
%% X = 2xTS cell, 2 inputs over TS timsteps
% Each X{1,ts} = 1xQ matrix, input #1 at timestep ts.
% Each X{2,ts} = 1xQ matrix, input #2 at timestep ts.
%% Xi = 2x1 cell 2, initial 1 input delay states.
% Each Xi{1,ts} = 1xQ matrix, initial states for input #1.
% Each Xi{2,ts} = 1xQ matrix, initial states for input #2.
%% Ai = 2x0 cell 2, initial 1 layer delay states.
% Each Ai{1,ts} = 20xQ matrix, initial states for layer #1.
% Each Ai{2,ts} = 1xQ matrix, initial states for layer #2.
%% and returns:
% Y = 1xTS cell of 2 outputs over TS timesteps.
% Each Y{1,ts} = 1xQ matrix, output #1 at timestep ts.
%% Xf = 2x1 cell 2, final 1 input delay states.
% Each Xf{1,ts} = 1xQ matrix, final states for input #1.
% Each Xf{2,ts} = 1xQ matrix, final states for input #2.
%% Af = 2x0 cell 2, final 0 layer delay states.
% Each Af{1ts} = 20xQ matrix, final states for layer #1.
% Each Af{2ts} = 1xQ matrix, final states for layer #2.
%% where Q is number of samples (or series) and TS is the number of timesteps.
%#ok<*RPMT0>
% ===== NEURAL NETWORK CONSTANTS =====
% Input 1
x1_step1_xoffset = 1;x1_step1_gain = 0.00549450549450549;x1_step1_ymin = -1;% Input 2
x2_step1_xoffset = -21480;x2_step1_gain = 2.97129138266073e-06;x2_step1_ymin = -1;% Layer 1
b1 = [-9.3839221387106413;6.3673650679944052;10.198031952946559;-5.6065382458804498;1.4990163108572789;5.1188396872625352;-0.90118631104219182;-0.30494277554968652;-1.697067083760901;-1.6272737005091373;6.3305489926853982;3.0484646453328987;-1.6579644194560408;4.3743984158143681;7.539389327001226;-1.3832313531170317;6.6047117902635213;-10.14216902642435;5.7011344366241081;7.0957086293236227];IW1_1 = [2.0258887755500834;-4.2028504717535542;-8.5464197976148668;2.5080209301563365;-3.9463681010808207;-2.5370763473999776;1.4061148692807672;1.5181842968841124;-3.1441033960841618;-7.8548645123947756;0.0091129562548408864;3.3948767243040034;-1.7058048527967125;5.3377573195333579;2.3789548426459328;3.3080234966837332;5.0430922734647279;-10.570582147844345;2.1165050263862364;4.0310619147913478];IW1_2 = [-3.4365907728390428;11.567104200163792;-0.1263365421914415;6.3186864593347876;8.2894556267593753;12.219941806571244;-0.8716215531914453;4.94787248538118;4.2165948351438223;-8.525057555843965;13.111856679144475;-1.1950749050600189;0.23506905319499458;-9.9752226612737704;-10.615461396517761;-4.8799419207110208;-1.6176406051352417;-0.6096358833405241;-6.0675879268844328;-4.3083059524580802];% Layer 2
b2 = 2.5640990680145257;LW2_1 = [0.6545727832513627 -0.18920916505217597 -1.2655928487990731 -0.58900624069322183 0.392454543501077 0.11816796602420446 -0.65362987999478428 0.25904685510735481 0.11936289461658904 0.13118489685451218 -0.17113982960225663 -1.068411519863667 -1.5868322036805684 0.022550373062452611 0.60270331566362767 0.58077892473389758 -0.47028353992483191 -0.20473592291391843 -2.7571898591909338 0.93992224806624713];% Output 1
y1_step1_ymin = -1;y1_step1_gain = 2.97129138266073e-06;y1_step1_xoffset = -21480;% ===== SIMULATION ========
% Format Input Arguments
isCellX = iscell(X);if ~isCellX, X = {X}; end;if (nargin < 2), error('Initial input states Xi argument needed.'); end% Dimensions
TS = size(X,2); % timesteps
if ~isempty(X) Q = size(X{1},2); % samples/series
elseif ~isempty(Xi) Q = size(Xi{1},2);else Q = 0;end% Input 1 Delay States
Xd1 = cell(1,2);for ts=1:1 Xd1{ts} = mapminmax_apply(Xi{1,ts},x1_step1_gain,x1_step1_xoffset,x1_step1_ymin);end% Input 2 Delay States
Xd2 = cell(1,2);for ts=1:1 Xd2{ts} = mapminmax_apply(Xi{2,ts},x2_step1_gain,x2_step1_xoffset,x2_step1_ymin);end% Allocate Outputs
Y = cell(1,TS);% Time loop
for ts=1:TS % Rotating delay state position
xdts = mod(ts+0,2)+1; % Input 1 Xd1{xdts} = mapminmax_apply(X{1,ts},x1_step1_gain,x1_step1_xoffset,x1_step1_ymin); % Input 2 Xd2{xdts} = mapminmax_apply(X{2,ts},x2_step1_gain,x2_step1_xoffset,x2_step1_ymin); % Layer 1 tapdelay1 = cat(1,Xd1{mod(xdts-1-1,2)+1}); tapdelay2 = cat(1,Xd2{mod(xdts-1-1,2)+1}); a1 = tansig_apply(repmat(b1,1,Q) + IW1_1*tapdelay1 + IW1_2*tapdelay2); % Layer 2 a2 = repmat(b2,1,Q) + LW2_1*a1; % Output 1 Y{1,ts} = mapminmax_reverse(a2,y1_step1_gain,y1_step1_xoffset,y1_step1_ymin); end% Final Delay States
finalxts = TS+(1: 1);xits = finalxts(finalxts<=1);xts = finalxts(finalxts>1)-1;Xf = [Xi(:,xits) X(:,xts)];Af = cell(2,0);% Format Output Arguments
if ~isCellX, Y = cell2mat(Y); endend% ===== MODULE FUNCTIONS ========
% Map Minimum and Maximum Input Processing Function
function y = mapminmax_apply(x,settings_gain,settings_xoffset,settings_ymin)y = bsxfun(@minus,x,settings_xoffset);y = bsxfun(@times,y,settings_gain);y = bsxfun(@plus,y,settings_ymin);end% Sigmoid Symmetric Transfer Function
function a = tansig_apply(n)a = 2 ./ (1 + exp(-2*n)) - 1;end% Map Minimum and Maximum Output Reverse-Processing Function
function x = mapminmax_reverse(y,settings_gain,settings_xoffset,settings_ymin)x = bsxfun(@minus,y,settings_ymin);x = bsxfun(@rdivide,x,settings_gain);x = bsxfun(@plus,x,settings_xoffset);end
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