MATLAB Implementation of Probabilistic Neural Network

The following MATLAB source code implements a Probabilistic Neural Network (PNN), which you can utilize for accurate pattern recognition and data classification. This implementation includes comprehensive functionality covering the essential workflow of PNN development: - Data preprocessing: Includes normalization routines and feature scaling to prepare input data for optimal network performance - Model training: Implements the probabilistic neural network architecture using radial basis functions and Parzen window density estimation - Model evaluation: Contains performance metrics calculation including classification accuracy, confusion matrix analysis, and probability calibration - Result visualization: Provides graphical outputs for decision boundaries, probability distributions, and classification results These components represent the core implementation steps for building an effective probabilistic neural network. The code structure allows for clear understanding of PNN's working mechanism, featuring modular design that enables easy customization and adaptation to specific application requirements. Key MATLAB functions employed include patternnet for network creation, train for model optimization, and various statistical tools for probability calculations.