MATLAB Implementation Divided into Two Parts: PCA with Rough Sets and Fuzzy Neural Networks

In this documentation, we present a MATLAB-based computer program designed to assist researchers in pattern recognition and face recognition studies. The implementation is structured into two distinct parts. The first part implements a face recognition system using Principal Component Analysis (PCA) for dimensionality reduction, Rough Set theory for feature selection, and Fuzzy Neural Networks for classification. The second part implements a pattern recognition system that combines PCA and Linear Discriminant Analysis (LDA) for feature extraction, followed by Rough Set processing and Fuzzy Neural Network classification. The code includes modules for handling the ORL face database, with implementations for data preprocessing, feature transformation using eigenvalue decomposition, and neural network training with fuzzy logic rules. Our experimental results demonstrate the effectiveness of these hybrid approaches, providing valuable insights for researchers interested in computational intelligence methods for recognition tasks.