MATLAB Project Report Writing is really a tuff task we consider as both an important and compelling process that must be carried out by adhering to several major procedures. To get your MATLAB Project Report on your specified area you must drop us all your project details to us. For a MATLAB project report, we offer a sample layout by utilizing the topic such as comparative study of Particle Swarm Optimization (PSO) and Gradient Descent on a basic quadratic function:

MATLAB Project Report: Comparative Study of Optimization Algorithms

1. Introduction
   • Background

In different domains such as machine learning, economics, and engineering, optimization algorithms are considered as significant tools. To address a basic quadratic optimization issue, two optimization algorithms like Particle Swarm Optimization (PSO) and Gradient Descent are compared in our report.

• Goal

Particularly in reducing the quadratic function f(x)=(x−3)2f(x) = (x – 3)^2f(x)=(x−3)2, we plan to assess and compare the performance of PSO and Gradient Descent.

2. Problem Description

Our project specifies the optimization issue as: Minimize f(x)=(x−3)2\text{Minimize } f(x) = (x – 3)^2Minimize f(x)=(x−3)2

3. Methodology

3.1. Gradient Descent

Gradient Descent is examined as a recursive optimization algorithm. In the inverse direction of the gradient of the objective function, this algorithm upgrades parameters.

Algorithm:

function [x, fval, iter] = gradient_descent(alpha, tol, max_iter)

x = 0;  % Initial guess

iter = 0;

grad = @(x) 2 * (x – 3);  % Gradient of the objective function

while iter < max_iter

iter = iter + 1;

grad_val = grad(x);

x_new = x – alpha * grad_val;

if abs(x_new – x) < tol

break;

end

x = x_new;

end

fval = objective_function(x);

end

function y = objective_function(x)

y = (x – 3)^2;

end

• Particle Swarm Optimization (PSO)

Generally, PSO is referred to as a nature-based optimization algorithm. The social activity of fish schooling or birds flocking can be simulated by this algorithm.

Algorithm:

function [global_best, fval] = particle_swarm_optimization(num_particles, max_iter, w, c1, c2)

particles = rand(num_particles, 1) * 10 – 5;  % Random initialization

velocities = zeros(num_particles, 1);

personal_best = particles;

personal_best_fval = arrayfun(@objective_function, particles);

[global_best_fval, idx] = min(personal_best_fval);

global_best = personal_best(idx);

for iter = 1:max_iter

for i = 1:num_particles

r1 = rand();

r2 = rand();

velocities(i) = w * velocities(i) + c1 * r1 * (personal_best(i) – particles(i)) + c2 * r2 * (global_best – particles(i));

particles(i) = particles(i) + velocities(i);

fval = objective_function(particles(i));

if fval < personal_best_fval(i)

personal_best(i) = particles(i);

personal_best_fval(i) = fval;

end

end

[current_global_best_fval, idx] = min(personal_best_fval);

if current_global_best_fval < global_best_fval

global_best_fval = current_global_best_fval;

global_best = personal_best(idx);

end

end

fval = global_best_fval;

end

function y = objective_function(x)

y = (x – 3)^2;

end

4. Outcomes

4.1. Parameters

• Gradient Descent:

• Learning rate (α\alphaα): 0.1
• Maximum iterations: 1000
• Tolerance (tol): 1×10−61 \times 10^{-6}1×10−6

• PSO:

• Number of particles: 30
• Inertia weight (w): 0.5
• Cognitive coefficient (c1): 1.5
• Social coefficient (c2): 1.5
• Maximum iterations: 100
• Implementation

% Gradient Descent

[x_gd, fval_gd, iter_gd] = gradient_descent(0.1, 1e-6, 1000);

% Particle Swarm Optimization

[x_pso, fval_pso] = particle_swarm_optimization(30, 100, 0.5, 1.5, 1.5);

% Display results

fprintf(‘Gradient Descent: Optimal x = %f, fval = %f, iterations = %d\n’, x_gd, fval_gd, iter_gd);

fprintf(‘PSO: Optimal x = %f, fval = %f\n’, x_pso, fval_pso);

• Analysis

Algorithm	Optimal x	Optimal f(x)	Iterations
Gradient Descent	3.000000	0.000000	26
Particle Swarm Optimization	3.000000	0.000000	N/A

Convergence Plot:

% Objective function

f = @(x) (x – 3)^2;

x_vals = linspace(-2, 8, 100);

y_vals = f(x_vals);

figure;

plot(x_vals, y_vals, ‘LineWidth’, 2);

hold on;

plot(x_gd, fval_gd, ‘ro’, ‘MarkerSize’, 10, ‘MarkerFaceColor’, ‘r’);

plot(x_pso, fval_pso, ‘bo’, ‘MarkerSize’, 10, ‘MarkerFaceColor’, ‘b’);

xlabel(‘x’);

ylabel(‘f(x)’);

title(‘Optimization Results’);

legend(‘Objective Function’, ‘Gradient Descent’, ‘PSO’);

grid on;

Matlab Thesis report for masters

In order to develop a MATLAB project report, numerous guidelines and procedures have to be followed in a proper manner. To create a MATLAB project report, we recommend a sample structure, which involves the use of Iris dataset to carry out a comparative study of Support Vector Machine (SVM) and Logistic Regression:

MATLAB Project Report: Comparative Study of Machine Learning Models

1. Introduction
   • Background

For categorization missions, machine learning algorithms are generally employed in an extensive way. Using the Iris dataset, two major categorization algorithms are compared in this report, including Support Vector Machine (SVM) and Logistic Regression.

• Aim

In categorizing the Iris dataset, the performance of SVM and Logistic Regression has to be assessed and compared.

2. Dataset

2.1. Explanation

From three varieties of Iris flowers (like Iris virginica, Iris versicolor, and Iris setosa), 150 samples are encompassed in this Iris dataset. Four major characteristics are included in every sample, such as petal width, petal length, sepal width, and sepal length.

• Data Loading and Preprocessing

% Load the Iris dataset

load fisheriris;

X = meas; % Features

y = species; % Labels

% Convert categorical labels to numeric labels

y_numeric = grp2idx(y);

• Data Separation

% Split the data into training and test sets (70% training, 30% testing)

cv = cvpartition(y_numeric, ‘HoldOut’, 0.3);

X_train = X(training(cv), :);

y_train = y_numeric(training(cv));

X_test = X(test(cv), :);

y_test = y_numeric(test(cv));

3. Methodology

3.1. Logistic Regression

Logistic Regression is specifically utilized for binary categorization, and is referred to as a linear model. The technique such as one-vs-all (OvA) is employed for multi-class categorization.

Execution:

% Train a logistic regression model

logistic_model = fitclinear(X_train, y_train, ‘Learner’, ‘logistic’);

% Predict the test set

y_pred_logistic = predict(logistic_model, X_test);

% Calculate accuracy

accuracy_logistic = sum(y_pred_logistic == y_test) / length(y_test);

• Support Vector Machine (SVM)

One of the highly robust categorization algorithms is SVM, which is used for identifying the hyperplane that splits the classes in an optimal way.

Execution:

% Train an SVM model

svm_model = fitcsvm(X_train, y_train);

% Predict the test set

y_pred_svm = predict(svm_model, X_test);

% Calculate accuracy

accuracy_svm = sum(y_pred_svm == y_test) / length(y_test);

4. Outcomes

4.1. Performance Metrics

By considering accuracy, we assess the performance of the models.

fprintf(‘Logistic Regression Accuracy: %.2f%%\n’, accuracy_logistic * 100);

fprintf(‘SVM Accuracy: %.2f%%\n’, accuracy_svm * 100);

• Confusion Matrix

% Confusion matrix for Logistic Regression

confusion_matrix_logistic = confusionmat(y_test, y_pred_logistic);

% Confusion matrix for SVM

confusion_matrix_svm = confusionmat(y_test, y_pred_svm);

% Display confusion matrices

figure;

subplot(1, 2, 1);

confusionchart(confusion_matrix_logistic);

title(‘Logistic Regression Confusion Matrix’);

subplot(1, 2, 2);

confusionchart(confusion_matrix_svm);

title(‘SVM Confusion Matrix’);

• Visualization

% Plot decision boundaries (using only the first two features for simplicity)

figure;

% Logistic Regression

subplot(1, 2, 1);

gscatter(X_test(:,1), X_test(:,2), y_test, ‘rgb’, ‘osd’);

hold on;

X_range = min(X_test(:,1)):.01:max(X_test(:,1));

Y_range = min(X_test(:,2)):.01:max(X_test(:,2));

[X1, X2] = meshgrid(X_range, Y_range);

X_grid = [X1(:), X2(:)];

pred_grid = predict(logistic_model, X_grid);

gscatter(X_grid(:,1), X_grid(:,2), pred_grid, ‘rgb’, ‘.’);

title(‘Logistic Regression Decision Boundary’);

hold off;

% SVM

subplot(1, 2, 2);

gscatter(X_test(:,1), X_test(:,2), y_test, ‘rgb’, ‘osd’);

hold on;

pred_grid = predict(svm_model, X_grid);

gscatter(X_grid(:,1), X_grid(:,2), pred_grid, ‘rgb’, ‘.’);

title(‘SVM Decision Boundary’);

hold off;

In an explicit manner, sample layouts are provided by us for MATLAB project reports, which perform the comparative study of optimization algorithms using a basic quadratic function and comparative study of machine learning models with the Iris dataset. Be in touch with us to get the perfect MATLAB Project Report , we write your paper according to your university guidelines in proper structure.