Explore MATLAB source code curated for "近红外光谱" with clean implementations, documentation, and examples.
Leveraging functions from MATLAB's Neural Network Toolbox, this approach employs both Backpropagation (BP) and Radial Basis Function (RBF) neural networks to develop mathematical models correlating near-infrared spectra of gasoline samples with their octane ratings. The implementation includes performance evaluation metrics for model validation.
Supervised Learning Neural Network Regression for Gasoline Octane Number Prediction Using Near-Infrared Spectroscopy Data. For detailed tutorial explanations and high-resolution materials (due to file size limitations), please contact me at 1066146635@qq.com. This implementation covers neural network architecture design, spectral data preprocessing, and regression model training techniques.
This resource covers Extreme Learning Machine applications for regression and classification, featuring practical implementations including gasoline octane prediction using near-infrared spectroscopy and iris species recognition with comprehensive code examples for learning purposes.
Techniques and Algorithms for Feature Wavelength Selection in NIR Spectroscopy Data Analysis
Implementation of Mahalanobis Distance Calculation with Code Integration for Data Analysis