Sample Problem % Half Moon Classifier using RBF % NN toolbox clc; clear all; close all; %% data load('HalfmoonData.mat'); rad = 10; % central radius of the half...

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Sample Problem

% Half Moon

Classifier using RBF

% NN toolbox

clc;

clear all;

close all;

%% data

load('HalfmoonData.mat');

rad = 10;

% central radius of the half moon

width = 6;

% width of the half moon

dist = -4;

% distance between two half moons

% Normalize

data

meanD = [mean(halfmoonData(1:2,:)')';0];

% mean of the original data

maxD

= [max(abs(halfmoonData(1:2,:)'))';1];% max of the original data

norData = mapminmax(halfmoonData(:,:));

net = newrb(norData(1:2,:),norData(3,:),0.01); % Create new RBF NN

%% Test network performance

y = net(norData(1:2,:));

hold on;

% Plot classification results

figure;

hold on;

yOut = ones(1,length(norData));

for i = 1:length(y)

xx

= maxD(1:2,:).*norData(1:2,i) + meanD(1:2,:);

if y(:,i)>0

yOut(:,i) = 1;

plot(xx(1),xx(2),'r*');

end

if y(:,i)<>

yOut(:,i) = -1;

plot(xx(1),xx(2),'ko');

end

end

xlabel('x');ylabel('y');

title(['Classification using MLP with dist = ',num2str(dist), ', radius = ',...

num2str(rad), ' and width = ',num2str(width)]);

% Calculate

accuracy

accuracy = perfCalc(y,halfmoonData(3,:),2)

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Answered 1 days After Mar 18, 2023

Solution

Sathishkumar answered on Mar 20 2023

25 Votes

SOLUTION.PDF

Sample Problem % Half Moon Classifier using RBF % NN toolbox clc; clear all; close all; %% data load('HalfmoonData.mat'); rad = 10; % central radius of the half...

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