Welcome to the official repository of Intelligent Control System Practicum Jobsheets. This project provides hands-on and beginner-friendly tutorials in Python for university-level students focusing on:
- Fuzzy Logic Control
- Artificial Neural Networks (ANN)
All materials are presented as Jupyter Notebooks with illustrative examples, diagrams, and embedded images to ensure interactive and effective learning.
.
├── LICENSE
├── README.md
├── requirements.txt
└── Jobsheet
├── haarcascade_frontalface_default.xml # For CV examples (used in some advanced notebooks)
├── images/ # Supporting diagrams and illustrations
├── jobsheet-skc-fuzzy-01a.ipynb # Fuzzy basics: membership functions
├── jobsheet-skc-fuzzy-01b.ipynb # Fuzzy membership function implementation
├── jobsheet-skc-fuzzy-02a.ipynb # Fuzzy inference system (FIS)
├── jobsheet-skc-fuzzy-02b.ipynb # Manual FIS implementation
├── jobsheet-skc-fuzzy-02c.ipynb # Advanced FIS with Python packages
├── jobsheet-skc-fuzzy-03a.ipynb # Case study: Temperature control using fuzzy logic
├── jobsheet-skc-fuzzy-03b.ipynb # Homework extension: Fuzzy fan controller
├── jobsheet-skc-nn-01a.ipynb # Perceptron: Introduction & Implementation
├── jobsheet-skc-nn-02a.ipynb # Multilayer Perceptron (MLP) architecture
├── jobsheet-skc-nn-02b.ipynb # Training MLP with backpropagation
├── jobsheet-skc-nn-02c.ipynb # Real-world application: Fruit image classification
├── jobsheet-skc-nn-03a.ipynb # Face detection using OpenCV and Haarcascade
├── jobsheet-skc-nn-03b.ipynb # CNN Introduction (theory only)
├── jobsheet-skc-nn-03c.ipynb # (Optional) CNN Implementation using Keras (future work)
└── jobsheet-skc-nn-03d.ipynb # Final Project Guidelines and Wrap-Up
| Jobsheet | Topic |
|---|---|
01a |
Introduction to Fuzzy Logic and Membership Functions |
01b |
Triangle, Trapezoidal, Gaussian Membership Implementation |
02a |
Fuzzy Inference System (FIS) with Rule Base |
02b |
Manual FIS Design: Step-by-step |
02c |
Using scikit-fuzzy and Matplotlib for Simulation |
03a |
Case Study: Temperature & Fan Control using Fuzzy |
03b |
Home Assignment: Modify for Real-Time Fan System |
| Jobsheet | Topic |
|---|---|
01a |
Single-Layer Perceptron |
02a |
Multilayer Perceptron (MLP) Theory |
02b |
Backpropagation and Activation Functions |
02c |
Application: Apple vs Orange Classifier |
03a |
Face Detection using OpenCV Haarcascade |
03b |
CNN Introduction: Structure and Intuition |
03c |
CNN Implementation using Keras (WIP) |
03d |
Final Project Guidelines for Students |
Some visual illustrations included in the jobsheets:
- Python 3.8 or newer
- Jupyter Notebook / JupyterLab
- Required packages: see
requirements.txt
pip install -r requirements.txt- Use Anaconda or Miniconda for managing virtual environments.
- Launch with:
jupyter notebookAfter completing these jobsheets, students will be able to:
- Understand and implement fuzzy membership functions
- Build simple fuzzy inference systems (manual and automated)
- Understand neural network architectures (Perceptron & MLP)
- Train and validate simple ANN models using Python
- Apply OpenCV for computer vision-based control systems
- Use Python as a simulation tool for intelligent control systems
This project is licensed under the MIT License.
This repository is maintained for academic and educational purposes by:
If you find this repository helpful:
- ⭐ Star this repo
- 🐛 Open issues for bug reports or suggestions
- 📩 Pull requests are welcome!
If you use this project in your teaching or research, please consider citing or crediting it in your documentation.
@misc{controlsystem-jobsheets,
author = {Ardy Seto Priambodo},
title = {Control System Practicum Jobsheets with Python},
year = {2024},
howpublished = {\url{https://github.com/2black0/Control-System-Practicum-Jobsheets-using-Python}},
}