🧪 Implementing Artificial Neural Network in Matlab

In this article, we will explore how to implement an artificial neural network (ANN) in Matlab to predict certain outcomes. We will specifically focus on using the Neural Network Fitting Tool in Matlab for this purpose. Let's dive right in!

1. Introduction

Before we get started, let's understand the context and objective of our neural network model. We have a priority list of buses in a 33-bus system according to user needs, with different loads assigned to each bus. To meet these load requirements, we have two sources for power generation - solar panels and wind turbines.

2. Defining the Data

In order to train our neural network, we need to define the input and output variables. The inputs will consist of the solar panel power and wind turbine power, while the outputs will be the corresponding voltages for each bus. We will start by defining the data sets for training and testing the neural network.

3. Setting Up the Neural Network Fitting Tool

To begin the implementation process, we will open the Neural Network Fitting Tool in Matlab. This tool provides a user-friendly interface for training and evaluating neural networks. We will import our defined variables and configure the tool for our specific application.

4. Training and Testing Percentage

Before we proceed with the training process, we need to decide the percentage of data we want to use for training and testing. This involves choosing how much data will be used for training the neural network and how much will be reserved for validation and testing purposes. We will discuss the recommended percentages and their significance.

5. Number of Hidden Neurons

The number of hidden neurons plays a crucial role in determining the performance of the neural network. This parameter affects the network's ability to learn and generalize Patterns from the input data. We will discuss the significance of this parameter and the recommended approach for selecting an appropriate number of hidden neurons.

6. Selecting the Training Algorithm

The choice of the training algorithm used in the neural network greatly impacts its accuracy and convergence. We will explore different algorithms available in Matlab, with a focus on the Levenberg-Marquardt algorithm, known for its accuracy and popularity. We will discuss the considerations and best practices for selecting the appropriate algorithm for our neural network model.

7. Evaluating the Results

Once the training process is complete, we need to evaluate the results to assess the accuracy and performance of our neural network. We will analyze key metrics such as mean squared error and regression to determine the quality of the model. Furthermore, we will plot regression graphs to Visualize the relationship between the predicted and actual outputs.

8. Simulink Diagram and Code Generation

In addition to the Neural Network Fitting Tool, Matlab provides Simulink, a graphical programming environment for modeling, simulating, and analyzing dynamic systems. We will explore how to create a Simulink diagram based on our trained neural network and generate corresponding Matlab code for further analysis or deployment.

9. Testing the Predictive Output

To verify the accuracy of our neural network model, we will perform a test using predetermined input values. We will input specific load demands and bus numbers and extract the predicted output values for solar panel and wind turbine powers. This real-time testing will demonstrate the practical application of our neural network.

10. Retraining the Neural Network

In case the results of our initial training are not satisfactory, we have the option to retrain the neural network to achieve better accuracy. We will explain how to adjust the training settings and iterate the training process until desirable results are obtained. We will emphasize the importance of iteratively refining the model to improve its predictive capabilities.

Complete Tutorial: Implementing ANN in MATLAB-SIMULINK