simulateStandaloneNet.m

function [y1] = simulateStandaloneNet(x1)
%SIMULATESTANDALONENET neural network simulation function.
%
% Generated by Neural Network Toolbox function genFunction.
%
% [y1] = simulateStandaloneNet(x1) takes these arguments:
%   x = 1xQ matrix, input #1
% and returns:
%   y = 1xQ matrix, output #1
% where Q is the number of samples.

%#ok<*RPMT0>

  % ===== NEURAL NETWORK CONSTANTS =====

  % Input 1
  x1_step1_xoffset = 0;
  x1_step1_gain = 0.200475452649894;
  x1_step1_ymin = -1;

  % Layer 1
  b1 = [6.0358701949520981;2.725693924978148;0.58426771719145909;-5.1615078566382975];
  IW1_1 = [-14.001919491063946;4.90641117353245;-15.228280764533135;-5.264207948688032];

  % Layer 2
  b2 = -0.75620725148640833;
  LW2_1 = [0.5484626432316061 -0.43580234386123884 -0.085111261420612969 -1.1367922825337915];

  % Output 1
  y1_step1_ymin = -1;
  y1_step1_gain = 0.2;
  y1_step1_xoffset = 0;

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

  % Dimensions
  Q = size(x1,2); % samples

  % Input 1
  xp1 = mapminmax_apply(x1,x1_step1_gain,x1_step1_xoffset,x1_step1_ymin);

  % Layer 1
  a1 = tansig_apply(repmat(b1,1,Q) + IW1_1*xp1);

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

  % Output 1
  y1 = mapminmax_reverse(a2,y1_step1_gain,y1_step1_xoffset,y1_step1_ymin);
end

% ===== 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