I am assumes you have a function bpnn_predict which takes two inputs and produces an output, and you wish to plot the results for a range of these inputs:
% Define the range of inputs for the two input variables
input1_range = linspace(min_input1, max_input1, num_points);
input2_range = linspace(min_input2, max_input2, num_points);
% Create a grid of input values
[Input1, Input2] = meshgrid(input1_range, input2_range);
% Initialize the matrix to hold the BPNN outputs
Output = zeros(size(Input1));
% Loop over each input combination and compute the BPNN output
for i = 1:numel(Input1)
Output(i) = bpnn_predict(Input1(i), Input2(i), constant_input3); % constant_input3 is the third input held constant
end
% Create the surface plot
surf(Input1, Input2, Output)
xlabel('Input 1')
ylabel('Input 2')
zlabel('Output')
title('BPNN Output Surface')
To diagnose overfitting, you can compare this surface plot with one generated from validation data that the network hasn't seen during training. If the surface looks overly complex or if it changes drastically when compared with the validation data, it might be a sign of overfitting.
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