Assignment: Understanding and Implementing Perceptron and Adaline Networks in MATLAB

Objective:

The objective of this assignment is to gain a deep understanding of the Perceptron and Adaline networks, implement them from scratch in MATLAB, and analyze their performance on a given dataset.

Implementation

Perceptron Implementation

Implement the Perceptron learning algorithm from scratch in MATLAB.

Train the Perceptron on a simple linearly separable dataset $($e$.$g$.,$ OR or AND gate$).$

Plot the decision boundary at each iteration to visualize how the Perceptron learns.

Adaline Implementation

Implement the Adaline learning algorithm from scratch in MATLAB.

Train the Adaline on the same dataset used for the Perceptron.

Plot the cost function $($mean squared error$)$ over iterations to show the learning process.

Experimentation

Dataset for Comparison

Use a more complex dataset $($e$.$g$.,$ Iris dataset or a synthetic dataset with more noise$).$

Split the dataset into training and testing sets.

Performance Comparison

Train both the Perceptron and Adaline on the training set.

Evaluate their performance on the testing set using appropriate metrics $($accuracy$,$ precision, recall, etc.$).$

Compare and discuss the results.

Analysis and Reporting

Analysis

Discuss the convergence behavior of both algorithms.

Analyze the impact of learning rate and number of iterations on the performance of Adaline.

Comment on the decision boundaries formed by Perceptron and Adaline.

Report

Prepare a detailed report documenting the theory, implementation, experimentation, and analysis.

Include code snippets, plots, and performance metrics in the report.

Conclude with insights and learnings from the assignment.

Submission Guidelines:

Create a Pdf file, contains:

MATLAB code files for Perceptron and Adaline implementations.

MATLAB Live Script $($or equivalent$)$ with the experimentation and plots.

Submit the final report as a PDF document.

Sample Datasets:

Simple Linearly Separable Data:

OR Gate:

$($

$0$

$,$

$0$

$)$

$->$

$0$

$,$

$($

$0$

$,$

$1$

$)$

$->$

$1$

$,$

$($

$1$

$,$

$0$

$)$

$->$

$1$

$,$

$($

$1$

$,$

$1$

$)$

$->$

$1$

$(0,0)->0,(0,1)->1,(1,0)->1,(1,1)->1$

AND Gate:

$($

$0$

$,$

$0$

$)$

$->$

$0$

$,$

$($

$0$

$,$

$1$

$)$

$->$

$0$

$,$

$($

$1$

$,$

$0$

$)$

$->$

$0$

$,$

$($

$1$

$,$

$1$

$)$

$->$

$1$

$(0,0)->0,(0,1)->0,(1,0)->0,(1,1)->1$

Complex Dataset:

Iris Dataset $($available from UCI Machine Learning Repository$)$