MATLAB Source Code for Classification and Recognition Using BP Neural Network Classifier

The following presents MATLAB source code for classification and recognition using a BP Neural Network classifier. This code offers high flexibility and extensibility, making it suitable for various classification applications. The implementation begins by importing necessary libraries and datasets. Data preprocessing steps include feature extraction using dimensionality reduction techniques and normalization to scale input values between 0-1. The BP neural network model is then defined with configurable parameters like hidden layer size and activation functions (e.g., sigmoid or tanh), followed by training using gradient descent optimization with backpropagation algorithm. Model evaluation includes calculating accuracy metrics and confusion matrices. Finally, classification results are visualized through decision boundary plots or ROC curves, demonstrating the classifier's performance. These steps provide comprehensive guidance for implementing and understanding BP neural network classifiers in classification tasks.