Artificial Neural Network Programs and Training Datasets
Ready-to-run artificial neural network programs and training datasets after decompression
Building Radial Basis Function Neural Networks from Training Data and Generating Input-Output Grid Plots
Efficiently construct radial basis function neural networks using training datasets and visualize model performance through input-output grid plots with implementation insights.
Gender Classification Using BP Neural Network Based on Student Physical Data
Implementing a 2-input 1-output BP neural network trained with height and weight data from 30 male and 30 female students, achieving 90% accuracy in gender classification for given input data through backpropagation learning algorithm.
Vector Quantization of Speech Signals Using LBG Algorithm: Code Implementation and Analysis
Implementation of LBG algorithm for speech signal vector quantization. This package contains two main scripts: training.m processes training data to generate four initial codebooks using iterative splitting methodology, while quantizing.m performs vector quantization on target data through nearest-neighbor codevector matching. Additional custom functions support codebook initialization, distortion calculation, and quantization error analysis.
BP Neural Network for Predicting Personal Income Information
BP Neural Network for predicting personal income information using adult.data as training dataset and adult.test as testing dataset, with implementation of backpropagation algorithm for model optimization.
Building a Radial Basis Function Neural Network with Training Data and Plotting Input-Output Grid Diagrams
Training a radial basis function neural network using sample data and visualizing its input-output mapping through grid plots with implementation details