MPPT Technique Neural Network Circuit

Integration of Neural Network(NN) with InC circuit.

Integration of Neural Network(NN) with InC circuit.

This is the code for changing i/p and o/p as per your requirments...

function [Y,Xf,Af] = myNeuralNetworkFunction(X,~,~)
%MYNEURALNETWORKFUNCTION neural network simulation function.
%
% Auto-generated by MATLAB, 13-Nov-2024 15:05:49.
%
% [Y] = myNeuralNetworkFunction(X,~,~) takes these arguments:
%
%   X = 1xTS cell, 1 inputs over TS timesteps
%   Each X{1,ts} = Qx2 matrix, input #1 at timestep ts.
%
% and returns:
%   Y = 1xTS cell of 1 outputs over TS timesteps.
%   Each Y{1,ts} = Qx1 matrix, output #1 at timestep ts.
%
% 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 = [0.34146221813891;15.0104475071389];
x1_step1.gain = [0.00200170112993274;0.100104560887701];
x1_step1.ymin = -1;

% Layer 1
b1 = [4.4926755370075115437;3.7582508693957881185;-1.7178246808533850221;-0.42321728898381044681;-1.0121917497679715492;-0.5602599447159295476;0.98177130623752151273;1.0110391144616179826;3.8802463640733879657;-4.3159390076281392012];
IW1_1 = [-4.4373465039460890935 -0.091140891909045410046;-4.0198780905126039187 1.4224829521803272758;2.8676544763773348379 -3.8113641058862355315;1.8754270232383681537e-06 0.26642234920289015188;2.8116719440426085619 3.209660242645796302;-4.4195262035258426891 -1.2105949685752719347;-4.3878874281663111931e-06 0.43662968215577296904;-0.0018603078415276865735 -1.6459666922922007171;3.4841924243950130879 2.05207965616648913;-4.8574496319011446488 -1.0916554345038218532];

% Layer 2
b2 = -0.41226234203600070627;
LW2_1 = [5.4325352850174853691e-06 -3.9054222298266312848e-06 -2.066503219577792473e-06 -3.4163990961468950758 1.5140737145842605934e-07 8.7828638458003640692e-08 -1.2606508274188612528 -0.0035780854653371303199 5.2143431482006447628e-06 1.4632106345507084842e-06];

% Output 1
y1_step1.ymin = -1;
y1_step1.gain = 0.271278721139538;
y1_step1.xoffset = 27.0137535076954;

% ===== SIMULATION ========

% Format Input Arguments
isCellX = iscell(X);
if ~isCellX
    X = {X};
end

% Dimensions
TS = size(X,2); % timesteps
if ~isempty(X)
    Q = size(X{1},1); % samples/series
else
    Q = 0;
end

% Allocate Outputs
Y = cell(1,TS);

% Time loop
for ts=1:TS

    % Input 1
    X{1,ts} = X{1,ts}';
    Xp1 = mapminmax_apply(X{1,ts},x1_step1);

    % Layer 1
    a1 = tansig_apply(repmat(b1,1,Q) + IW1_1*Xp1);

    % Layer 2
    a2 = repmat(b2,1,Q) + LW2_1*a1;

    % Output 1
    Y{1,ts} = mapminmax_reverse(a2,y1_step1);
    Y{1,ts} = Y{1,ts}';
end

% Final Delay States
Xf = cell(1,0);
Af = cell(2,0);

% Format Output Arguments
if ~isCellX
    Y = cell2mat(Y);
end
end

% ===== MODULE FUNCTIONS ========

% Map Minimum and Maximum Input Processing Function
function y = mapminmax_apply(x,settings)
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)
x = bsxfun(@minus,y,settings.ymin);
x = bsxfun(@rdivide,x,settings.gain);
x = bsxfun(@plus,x,settings.xoffset);
end