Jaringan Syaraf Tiruan [2]: Model McCulloh-Pitts dan Hebb

Introduction

This tutorial will explore the concepts of Artificial Neural Networks (ANN) as presented in the YouTube video by Hidayat Erwin. We will delve into the McCulloch-Pitts model and the Hebb model, which are foundational in understanding how neural networks can recognize patterns, such as the letters T and U. By the end of this tutorial, you will have a clearer understanding of these models and their applications in neural networks.

Step 1: Understanding the McCulloch-Pitts Model

The McCulloch-Pitts model is one of the earliest models of artificial neurons. It simulates how neurons process inputs to produce outputs.

Key Concepts

• Neurons as Binary Units: Each neuron can either be activated (1) or not activated (0) based on the weighted sum of its inputs.
• Threshold Activation: A neuron fires only if the total input exceeds a certain threshold.

Practical Advice

• Create a Simple Model:

  1. Define inputs (e.g., binary values representing features of letters).
  2. Assign weights to these inputs.
  3. Set a threshold for activation.

• Example: For recognizing the letter 'T':

  • Input: [1, 0, 1] (representing features)
  • Weights: [0.5, 0.3, 0.7]
  • Threshold: 1.0
  • Calculation: If (10.5 + 00.3 + 1*0.7) > 1.0, then the neuron activates.

Step 2: Exploring the Hebb Model

The Hebb model is based on the principle of learning through reinforcement. It suggests that connections between neurons strengthen when they are activated together.

Key Concepts

• Synaptic Strength: The connection between neurons is strengthened when they fire simultaneously.

• Hebbian Learning Rule: The formula for adjusting weights is often represented as:

  Δw = η * x * y
  

  where Δw is the change in weight, η is the learning rate, x is the input, and y is the output.

Practical Advice

• Implementing Hebbian Learning:

  1. Initialize weights randomly.
  2. For each training example, apply the Hebbian learning rule to adjust weights.

• Example: If the input is [1, 0] and the output is also 1, increase the weights for the activated inputs.

Step 3: Applying Models to Recognize Letters

Both models can be utilized to recognize patterns such as letters T and U.

Practical Steps

• Data Preparation: Prepare a dataset with binary representations of letters.
• Training: Use the McCulloch-Pitts model for initial recognition and the Hebb model for reinforcement learning.
• Testing: Validate the models with unseen data to check their accuracy.

Conclusion

In this tutorial, we covered the McCulloch-Pitts and Hebb models, crucial components of artificial neural networks. Understanding these models provides a foundational perspective on how neural networks learn and recognize patterns.

Next Steps

• Explore more complex neural networks, such as multilayer perceptrons.
• Experiment with coding these models using a programming language like Python.
• Read the reference material for deeper insights into artificial intelligence and neural networks.

For further learning, consider watching additional videos or reading more literature on neural network architectures and their applications in modern AI.