This project implements a neural network in C++ to simulate various electronic circuits, including XOR, NAND, MUX, DEMUX, ADDER, and FLIP FLOP. The neural network is designed from scratch and provides a foundation for simulating different electronics circuits. This README provides an overview of the project and explains how to use it.

• Introduction
• Features
• Prerequisites
• Getting Started
• Usage
• Configuration
• Saving Results
• Contributing
• License
• Contact

This project is a neural network implemented in C++ from scratch. It simulates various electronic circuits, including XOR, NAND, MUX, DEMUX, ADDER, and FLIP FLOP, using artificial neural networks. The purpose of this project is to gain a deeper understanding of neural networks and their application in simulating electronic circuits.

• Simulates electronic circuits using a custom neural network implementation.
• Supports various electronic circuit simulations, including XOR, NAND, MUX, DEMUX, ADDER, and FLIP FLOP.
• Provides the flexibility to define and test different topologies for neural networks.
• Saves simulation results to text files for analysis and evaluation.

To run this project, you need:

• A C++ compiler (e.g., g++)
• CMake (for building)
• A basic understanding of neural networks and electronic circuits

1. Clone this repository to your local machine.
2. Build the project using CMake.
3. Run the compiled executable to simulate electronic circuits using the neural network.

You can create test files for various electronic circuits. Example Python scripts are provided for creating test files:

• XOR Test File: XOR.txt
• Flip-Flop Test File: FlipFlop.txt

Modify these scripts to generate test files for other electronic circuits if needed.

1. Compile and run the C++ program, providing the test file as input. For example:

   ./NeuralNet test/XOR.txt

2. The program will read the test file, simulate the electronic circuit using the neural network, and save the results in a text file.

3. The simulation results will include input values, output values, and error metrics.

You can configure the neural network topology, learning rate (eta), momentum (alpha), and other parameters in the C++ source code. The provided code is well-documented to guide you through the configuration process.

The program saves simulation results in text files in the "results" folder. You can change the filename and location in the C++ code to match your requirements.

If you wish to contribute to this project, feel free to fork the repository, make your changes, and submit a pull request.

This project is licensed under the MIT License. See the LICENSE file for details.

For any inquiries or feedback, please contact [[email protected]].