Use Generated Code to Accelerate an Application Deployed with MATLAB Compiler

function [tfe,err,cutoffFreq,ff] = ...
    myRLSFilterSystemIDSim(tuningUIStruct)
% myRLSFilterSystemIDSim implements the algorithm used in
% myRLSFilterSystemIDApp.
% This function instantiates, initializes, and steps through the System
% objects used in the algorithm.
%
% You can tune the cutoff frequency of the desired system and the
% forgetting factor of the RLS filter through the GUI that appears when
% myRLSFilterSystemIDApp is executed.

%   Copyright 2013-2017 The MathWorks, Inc.

%#codegen

% Instantiate and initialize System objects. The objects are declared
% persistent so that they are not recreated every time the function is
% called inside the simulation loop.
persistent rlsFilt sine unknownSys transferFunctionEstimator
if isempty(rlsFilt)
    % FIR filter models the unidentified system
    unknownSys = dsp.VariableBandwidthFIRFilter('SampleRate',1e4,...
        'FilterOrder',30,...
        'CutoffFrequency',.48 * 1e4/2);
    % RLS filter is used to identify the FIR filter
    rlsFilt = dsp.RLSFilter('ForgettingFactor',.99,...
        'Length',28);
    % Sine wave used to generate input signal
    sine = dsp.SineWave('SamplesPerFrame',1024,...
        'SampleRate',1e4,...
        'Frequency',50);
    % Transfer function estimator used to estimate frequency responses of
    % FIR and RLS filters.
    transferFunctionEstimator = dsp.TransferFunctionEstimator(...
        'FrequencyRange','centered',...
        'SpectralAverages',10,...
        'FFTLengthSource','Property',...
        'FFTLength',1024,...
        'Window','Kaiser');
end

if tuningUIStruct.Reset
    % reset System objects
    reset(rlsFilt);
    reset(unknownSys);
    reset(transferFunctionEstimator);
    reset(sine);
end

% Tune FIR cutoff frequency and RLS forgetting factor
if  tuningUIStruct.ValuesChanged
    param = tuningUIStruct.TuningValues;
    unknownSys.CutoffFrequency  = param(1);
    rlsFilt.ForgettingFactor = param(2);
end
    
% Generate input signal - sine wave plus Gaussian noise
inputSignal = sine() +  .1 * randn(1024,1);

% Filter input though FIR filter
desiredOutput = unknownSys(inputSignal);

% Pass original and desired signals through the RLS Filter
[rlsOutput , err] = rlsFilt(inputSignal,desiredOutput);

% Prepare system input and output for transfer function estimator
inChans = repmat(inputSignal,1,2);
outChans = [desiredOutput,rlsOutput];

% Estimate transfer function
tfe = transferFunctionEstimator(inChans,outChans);

% Save the cutoff frequency and forgetting factor
cutoffFreq = unknownSys.CutoffFrequency;
ff = rlsFilt.ForgettingFactor;

end

function scopeHandles = myRLSFilterSystemIDApp(numTSteps)
% myRLSFilterSystemIDApp initialize and execute RLS Filter
% system identification example. Then, display results using
% scopes. The function returns the handles to the scope and UI objects.
%
% Input:
%   numTSteps - number of time steps
% Outputs:
%   scopeHandles    - Handle to the visualization scopes

% Copyright 2013-2017 The MathWorks, Inc.


if nargin == 0
    numTSteps = Inf; % Run until user stops simulation.
end

% Create scopes
tfescope = dsp.ArrayPlot('PlotType','Line',...
    'Position',[8 696 520 420],...
    'YLimits',[-80 30],...
    'SampleIncrement',1e4/1024,...
    'YLabel','Amplitude (dB)',...
    'XLabel','Frequency (Hz)',...
    'Title','Desired and Estimated Transfer Functions',...
    'ShowLegend',true,...
    'XOffset',-5000);

msescope = timescope('SampleRate',1e4,...
    'Position',[8 184 520 420],...
    'TimeSpanSource','property','TimeSpan',0.01,...
    'YLimits',[-300 10],'ShowGrid',true,...
    'YLabel','Mean-Square Error (dB)',...
    'Title','RLSFilter Learning Curve');

screen = get(0,'ScreenSize');
outerSize = min((screen(4)-40)/2, 512);
tfescope.Position = [8, screen(4)-outerSize+8, outerSize+8,...
    outerSize-92];
msescope.Position = [8, screen(4)-2*outerSize+8, outerSize+8, ...
    outerSize-92];

% Create UI to tune FIR filter cutoff frequency and RLS filter
%  forgetting factor
Fs = 1e4;
param = struct([]);
param(1).Name = 'Cutoff Frequency (Hz)';
param(1).InitialValue = 0.48 * Fs/2;
param(1).Limits = Fs/2 * [1e-5, .9999];
param(2).Name = 'RLS Forgetting Factor';
param(2).InitialValue = 0.99;
param(2).Limits = [.3, 1];
hUI = HelperCreateParamTuningUI(param, 'RLS FIR Demo');
set(hUI,'Position',[outerSize+32, screen(4)-2*outerSize+8, ...
    outerSize+8, outerSize-92]);

% Execute algorithm
while(numTSteps>=0)
    
    S = HelperUnpackUIData(hUI);

    drawnow limitrate;   % needed to process UI callbacks
    
    [tfe,err] = myRLSFilterSystemIDSim(S);
    
    if S.Stop     % If "Stop Simulation" button is pressed
        break;
    end
    if S.Pause
        continue;
    end
    
    % Plot transfer functions
    tfescope(20*log10(abs(tfe)));
    % Plot learning curve
    msescope(10*log10(sum(err.^2)));
    numTSteps = numTSteps - 1;
end

if ishghandle(hUI)  % If parameter tuning UI is open, then close it.
    delete(hUI);
    drawnow;
    clear hUI
end

scopeHandles.tfescope = tfescope;
scopeHandles.msescope = msescope;
end

coder.screener('myRLSFilterSystemIDSim');