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Examples Gallery

Explore practical examples through interactive Colab notebooks organized by topic and difficulty.

🔰 Fundamentals (Beginner)

Learn the basics of fuzzy logic.

Membership Functions

Open In Colab

What you'll learn: - Triangular, trapezoidal, gaussian, sigmoid functions - FuzzySet and LinguisticVariable classes - Fuzzification process - Fuzzy operators (AND, OR, NOT)

Estimated time: 45-60 min

Thermal Comfort System

Open In Colab

What you'll learn: - Model multiple variables (temperature + humidity) - Combine variables with fuzzy operators - Implement simple IF-THEN rules - Create 2D comfort maps

Estimated time: 40-50 min

🎛️ Inference Systems (Intermediate)

Build complete fuzzy inference systems.

Mamdani Tipping System

Open In Colab

What you'll learn: - Complete Mamdani inference system - 5 Mamdani steps: fuzzification → rules → implication → aggregation → defuzzification - Multiple inputs (service + food quality) - 3D control surfaces

Estimated time: 60-75 min

Sugeno Zero-Order System

Open In Colab

What you'll learn: - Sugeno system with constant outputs - Difference between Mamdani and Sugeno - Weighted average defuzzification

Estimated time: 45-60 min

Sugeno First-Order System

Open In Colab

What you'll learn: - Sugeno with linear output functions: y = ax + b - Function approximation - Comparison with zero-order

Estimated time: 40-50 min

Voting Prediction

Open In Colab

What you'll learn: - Real-world application - Complex rule base - Multiple inputs (income + education)

Estimated time: 50-70 min

🧠 Learning & Optimization (Advanced)

Automatic rule generation and system optimization.

Wang-Mendel: Nonlinear Approximation

Open In Colab

What you'll learn: - Automatic rule generation from data - Single-pass learning algorithm - Function approximation: f(x) = sin(x) + 0.1x - Rule conflict resolution

Estimated time: 60-75 min

Wang-Mendel: Linear Function

Open In Colab

What you'll learn: - Simple case study - Effect of number of partitions - Performance metrics (MSE, RMSE, R²)

Estimated time: 40-50 min

Wang-Mendel: Iris Classification

Open In Colab

What you'll learn: - Classification with Wang-Mendel - Multi-class fuzzy classification - Interpretable fuzzy rules

Estimated time: 50-65 min

ANFIS: Iris Classification

Open In Colab

What you'll learn: - Adaptive Neuro-Fuzzy Inference System - Gradient-based learning (backpropagation) - Membership function refinement - Lyapunov stability monitoring

Estimated time: 60-75 min

ANFIS: Regression

Open In Colab

What you'll learn: - ANFIS for regression problems - Nonlinear function approximation - Comparison with neural networks

Estimated time: 50-65 min

Rules Optimization with PSO

Open In Colab

What you'll learn: - Particle Swarm Optimization (PSO) - Metaheuristic optimization - Optimize membership function parameters

Estimated time: 50-65 min

Rules Optimization: Iris

Open In Colab

What you'll learn: - Comparison: PSO vs DE vs GA - Classification optimization - Best practices

Estimated time: 55-70 min

🌊 Dynamic Systems (Advanced)

Fuzzy systems with time evolution.

p-Fuzzy Discrete: Predator-Prey

Open In Colab

What you'll learn: - Discrete p-fuzzy systems: x_{n+1} = x_n + f(x_n) - Population dynamics with fuzzy rules - Phase space analysis - Multiple initial conditions

Estimated time: 50-65 min

p-Fuzzy Continuous: Predator-Prey

Open In Colab

What you'll learn: - Continuous p-fuzzy: dx/dt = f(x) - ODE integration (Euler, RK4) - Oscillatory dynamics - Vector fields

Estimated time: 60-75 min

p-Fuzzy Discrete: Population Growth

Open In Colab

What you'll learn: - Single population model - Logistic-like fuzzy dynamics - Bifurcation analysis

Estimated time: 45-60 min

Fuzzy ODE: Logistic Growth

Open In Colab

What you'll learn: - ODEs with fuzzy parameters/initial conditions - α-level method for uncertainty propagation - Fuzzy envelopes

Estimated time: 55-70 min

Fuzzy ODE: Holling-Tanner

Open In Colab

What you'll learn: - System of ODEs with fuzzy uncertainty - Multi-dimensional envelopes - Phase space with uncertainty

Estimated time: 60-75 min

By Difficulty Level

  • Membership Functions
  • Thermal Comfort

🟡 Intermediate (After fundamentals)

  • All Inference Systems (Mamdani, Sugeno, Voting)

🔴 Advanced (Requires ML/math background)

  • All Learning notebooks (Wang-Mendel, ANFIS, PSO)
  • All Dynamics notebooks (p-fuzzy, Fuzzy ODEs)

Running the Examples

  1. Click any "Open in Colab" badge
  2. Run the first cell to install: !pip install pyfuzzy-toolbox
  3. Execute cells sequentially

Locally

# Clone repository
git clone https://github.com/1moi6/pyfuzzy-toolbox.git
cd pyfuzzy-toolbox/notebooks_colab

# Install dependencies
pip install pyfuzzy-toolbox jupyter

# Launch Jupyter
jupyter notebook

Need Help?

  • API Reference: Detailed documentation of all methods
  • User Guide: Conceptual explanations and tutorials
  • GitHub Issues: Report problems or ask questions