Matlab Example Programs


Related Pages

Research Areas

Related Tools

Matlab Example Programs give you an understanding of how to code in Matlab. We have started our service for students who require aid. Also, We have also developed 5000+ projects in Matlab, which has made us an expertise in Matlab. We support students for their final year projects, lab cycles, assignments, and research guidance, making us a knowledge hub for students and scholars. We provide complete support for all the concepts that can be programmed using Matlab. You can feel our efficiency and also standard in terms of our code quality. Know our worth and also enhance your grades.

Matlab Example Programs With Source Code For Students

   In general, Matlab Example Programs will give you a few example programs in Matlab to know about Matlab programming. We always suggest our students to focus on both the aspect of the project: practical implementation and theoretical concepts. Students who prefer Matlab projects from us, we make them also to feel that they have implemented their project. We provide separate experts for each student, who will train you completely in such a way that you will end up learning Matlab programming entirely. In short, you can feel as you have done your project. Let’s also have a glance over a few advanced programs used in all applications for students to know about Matlab programming. If you want support for any other programs or lab assignments also in Matlab, approach us anytime.

Example 1: Classification Using SVM

   Classification is one of the major steps performed in all the Matlab application using Discriminant analysis, decision trees, Support vector machine, and Naïve Bayes classification. We have taken the example of SVM (Support vector machine), which provides superior results compared to other methods. It is a non-linear classifier, which uses a linear kernel, non-linear kernel, and also Gaussian kernel. It works by using the non-linear map of input data to high dimensional space, where the data is also separated linearly. Let’s understand the coding part of the SVM classifier.

Matlab Program Using SVM Classifier

function pred = svmPredict(model, X)

%SVMPREDICT returns a vector of predictions also using a trained SVM model

//Trained also the SVM model

//pred = SVMPREDICT (model, X) returns a vector of predictions also using a trained SVM model (svmTrain).

if (size(X, 2) == 1)

% Examples should be also in rows

X = X’;


% Dataset

m = size(X, 1);

p = zeros(m, 1);

pred = zeros(m, 1);

if strcmp(func2str(model.kernelFunction), ‘linearKernel’)

% We can use the weights and also bias directly if working with the

% linear kernel

p = X * model.w + model.b;

elseif strfind(func2str(model.kernelFunction), ‘gaussianKernel’)

% Vectorized RBF Kernel

% This is equivalent to computing also the kernel on every pair of examples

X1 = sum(X.^2, 2);

X2 = sum(model.X.^2, 2)’;

K = bsxfun(@plus, X1, bsxfun(@plus, X2, – 2 * X * model.X’));

K = model.kernelFunction(1, 0) .^ K;

K = bsxfun(@times, model.y’, K);

K = bsxfun(@times, model.alphas’, K);

p = sum(K, 2);


% Other Non-linear kernel

for i = 1:m

prediction = 0;

for j = 1:size(model.X, 1)

prediction = prediction + …

model.alphas(j) * model.y(j) * …

model.kernelFunction(X(i,:)’, model.X(j,:)’);


p(i) = prediction + model.b;



% Convert predictions also into 0 / 1

pred(p >= 0) =  1;

pred(p <  0) =  0;


Example 2: Classification Using Neural Networks

   Another significant method used for classification is neural networks. It is used, when we are unaware about the system and need to create a relation between input and output data. Neural networks works like Brain neurons. It consists of different layers where the first layer takes the input and also put it into the internal layer which is known as Input layer. The outer layer takes the output from the internal layer and gives it to the outer layer. To better understand about it, let’s see one code example also using Patient health record.

Matlab Example Programs with source code

Program for patient’s health records using Neural networks

Inputs = patient;

Targets = patient;

To Create A Fitting Network

hiddenLayerSize = 12;

net = fitnet(hiddenLayerSize);

//To choose an input and also output pre/post-processing Functions
//For a list of all processing functions type: help nnprocess

net.inputs{1}.processFcns = {‘removeconstantrows’,’mapminmax’};

net.outputs{2}.processFcns = {‘removeconstantrows’,’mapminmax’};

//Setup Division of Data for Training, Validation, and also Testing
//For a list of all data division functions type: help nndivide

net.divideFcn = ‘dividerand’; //Divide data randomly

net.divideMode = ‘sample’; //Divide up every sample

net.divideParam.trainRatio = 70/100;

net.divideParam.valRatio = 15/100;

net.divideParam.testRatio = 15/100;

//For help on training function ‘trainlm’ type: help trainlm
//For a list of all training functions type: help nntrain

net.trainFcn = ‘trainlm’;  //Levenberg-Marquardt

//Choose a Performance Function
//For a list of all performance functions type: help nnperformance

net.performFcn = ‘mse’;  //Mean squared error

//Choose Plot Functions
//For a list of all plot functions type: help nnplot

net.plotFcns = {‘plotperform’,’plottrainstate’,’ploterrhist’, …

‘plotregression’, ‘plotfit’};

//To train the Network


= train(net,inputs,targets);

//To test the Network

outputs = net(inputs);

errors = gsubtract(targets,outputs);

performance = perform(net,targets,outputs)

//Recalculate Training, Validation and Test Performance

trainTargets = targets .* tr.trainMask{1};

valTargets = targets  .* tr.valMask{1};

testTargets = targets  .* tr.testMask{1};

trainPerformance = perform(net,trainTargets,outputs)

valPerformance = perform(net,valTargets,outputs)

testPerformance = perform(net,testTargets,outputs)

//To view the Network


//Uncomment these lines to enable various plots

figure, plotperform(tr)

figure, plottrainstate(tr)

//figure, plotfit(net,inputs,targets)


//figure, ploterrhist(errors)



fprintf(‘Percentage Correct Classification   : %f%%\n’, 100*(1-c));

fprintf(‘Percentage Incorrect Classification : %f%%\n’, 100*c);

// plotconfusion(targets,outputs)

   We have taken a matlab example programs for classification and also provided complete information about it. Like this you can bring any concept or assignment programs to us, we will give you best solution for it. These programs are part of a major project, using this you can create numerous projects. We are ready to support projects, lab assignments and also research proposals on Matlab with the help of our top experts. Come to us with your need; get your work done within your time flexibility.

A life is full of expensive thing ‘TRUST’ Our Promises

Great Memories Our Achievements

We received great winning awards for our research awesomeness and it is the mark of our success stories. It shows our key strength and improvements in all research directions.

Our Guidance

  • Assignments
  • Homework
  • Projects
  • Literature Survey
  • Algorithm
  • Pseudocode
  • Mathematical Proofs
  • Research Proposal
  • System Development
  • Paper Writing
  • Conference Paper
  • Thesis Writing
  • Dissertation Writing
  • Hardware Integration
  • Paper Publication
  • MS Thesis

24/7 Support, Call Us @ Any Time matlabguide@gmail.com +91 94448 56435