Explore MATLAB source code curated for "PNN" with clean implementations, documentation, and examples.
Classification Prediction using Probabilistic Neural Networks - Transformer Fault Diagnosis based on PNN, with MATLAB reference code implementation for neural network applications.
MATLAB implementation of Probabilistic Neural Network (PNN) with detailed code explanations and algorithmic insights
Detailed explanation of the establishment, model training, testing, and normalization processes for three neural network architectures, with performance evaluation and comparative analysis of different network performances, including result comparison images
The GMM-based Probabilistic Neural Network (PNN) demonstrates exceptional generalization capabilities, rapid learning efficiency, easy online updating, and is grounded in Bayesian estimation theory from statistics. It has become a highly effective tool for solving challenging classification problems such as speaker recognition, character recognition, medical image recognition, and satellite cloud pattern recognition. Notably, PNN not only inherits most advantages of GMM but also offers additional benefits including strong robustness, reduced training data requirements, and seamless integration with other networks and theories.
Implementation of several algorithms for handling class imbalance in classification problems, including PNN (Probabilistic Neural Network), SMOTE (Synthetic Minority Over-sampling Technique), and BP-AdaBoost (AdaBoost with Backpropagation Neural Network)
A speaker recognition program implementation based on Wavelet Neural Network PNN, featuring voice signal processing and pattern recognition capabilities with practical code examples for biometric authentication applications.
Implementation of three neural network methods for handwritten character recognition - Probabilistic Neural Network (PNN), Radial Basis Function Network (RBF), and Backpropagation Neural Network (BP) with code-level insights
PNN, also known as Probabilistic Neural Network, was initially proposed by mathematician Specht in 1990 and subsequently refined by researchers including Master [1995]. This network architecture has been successfully applied across multiple domains including machine learning, artificial intelligence, and automatic control systems. Compared to multi-layer feedforward networks, PNN demonstrates simpler mathematical principles and easier implementation through its probability density function estimation approach using Parzen windows and radial basis functions.
Implementation of image feature extraction using wavelet transforms followed by image classification through Probabilistic Neural Networks (PNN)
Technical comparison of Extreme Learning Machine (ELM) with Backpropagation Neural Network (BP), Radial Basis Function Network (RBF), Probabilistic Neural Network (PNN), and Generalized Regression Neural Network (GRNN)