What is a counterpropagation network (CPN) algorithm in C and how is it implemented?

Table of Contents

• Introduction
• How a Counterpropagation Network (CPN) Works
  • 1. CPN Architecture
  • 2. Training Process
  • 3. Propagation Phases
• Implementation of a Counterpropagation Network in C
  • C Code Example
  • Explanation:
• Conclusion

Introduction

A Counterpropagation Network (CPN) is a hybrid neural network that integrates two key components: the Kohonen Self-Organizing Map (SOM) for unsupervised learning and a Grossberg Outstar Network for supervised learning. This combination allows for efficient dimensionality reduction and pattern classification. CPN is used in applications such as function approximation and pattern recognition.

In this article, we’ll explain the working principles of a CPN and provide an implementation example in C.

How a Counterpropagation Network (CPN) Works

1. CPN Architecture

A Counterpropagation Network consists of three layers:

• Input Layer: Receives the input data.
• Kohonen Layer (Hidden Layer): This layer is responsible for unsupervised learning, where neurons compete to classify the input into clusters.
• Grossberg Layer (Output Layer): A supervised learning layer that maps the Kohonen neurons' outputs to target outputs.

2. Training Process

• Kohonen Layer: Uses competitive learning, where neurons "compete" based on a similarity metric such as Euclidean distance. The neuron closest to the input data is the winner and updates its weights.
• Grossberg Layer: This layer adjusts weights using supervised learning by mapping Kohonen neurons to specific target outputs.

3. Propagation Phases

• Forward Propagation: The input data is passed through the Kohonen layer, selecting the winner neuron, and then propagated to the Grossberg layer to generate the output.
• Backward Propagation: The supervised learning process in the Grossberg layer involves comparing the output with the target and adjusting weights based on the error.

Implementation of a Counterpropagation Network in C

Here’s a basic example to illustrate how to implement a CPN algorithm in C.

C Code Example

Explanation:

1. Euclidean Distance: The function calculates the distance between the input vector and the weight vector in the Kohonen layer.
2. Kohonen Layer: The function findWinningNeuron determines which neuron in the Kohonen layer is closest to the input pattern.
3. Grossberg Layer: Once the Kohonen layer identifies a winning neuron, the Grossberg layer adjusts its weights to map the input to the correct output.
4. Training Process: The network undergoes several epochs of training, where both the Kohonen and Grossberg layers are trained to map inputs to outputs.

Conclusion

The Counterpropagation Network (CPN) algorithm effectively combines unsupervised learning (Kohonen Layer) and supervised learning (Grossberg Layer), making it a versatile tool for classification and pattern recognition tasks. This C implementation of CPN illustrates how to set up a simple model for training and testing.