What is a feedforward neural network (FNN) algorithm in C and how is it implemented?

Table of Contents

• Introduction
• How Feedforward Neural Networks Work
  • 1. Architecture
  • 2. Training Process
• Feedforward Neural Network Implementation in C
  • Code Example
• Conclusion

Introduction

A Feedforward Neural Network (FNN) is a type of artificial neural network where the flow of information is unidirectional—from input to output—without cycles or loops. It’s one of the simplest yet foundational architectures in neural networks, often used for supervised learning tasks such as classification and regression. In this article, we'll explain the architecture and training process of FNNs and how to implement one in C.

How Feedforward Neural Networks Work

1. Architecture

An FNN consists of three main components:

• Input Layer: The first layer that receives the input data.
• Hidden Layers: One or more layers where neurons process the data through weighted connections.
• Output Layer: The final layer that produces the prediction or classification.

The neurons in each layer perform a weighted sum of inputs, and the result is passed through an activation function to introduce non-linearity into the model.

2. Training Process

Training an FNN involves adjusting the weights using a method called backpropagation:

• Forward Pass: Data flows from the input layer through hidden layers to the output layer.
• Error Calculation: The difference between the predicted and actual outputs is computed using a loss function.
• Backpropagation: The error is propagated backward, and weights are adjusted to minimize the error.

Feedforward Neural Network Implementation in C

Here’s a basic implementation of a simple Feedforward Neural Network in C using backpropagation.

Code Example

Conclusion

A Feedforward Neural Network (FNN) is the simplest type of neural network that is crucial in solving many machine learning problems. Implementing FNNs in C involves managing layers, forward passes, and backpropagation manually, as demonstrated in the example. This basic implementation of a neural network can be expanded to more complex architectures and larger datasets to solve real-world problems like classification and regression.