Supervised Learning Neural Network Regression Fitting - Gasoline Octane Number Prediction Based on Near-Infrared Spectroscopy

Supervised Learning Neural Network Regression Fitting - Gasoline Octane Number Prediction Based on Near-Infrared Spectroscopy

In this tutorial, we provide a comprehensive explanation of using neural networks for regression analysis to predict gasoline octane numbers based on near-infrared spectral data. This practical application domain involves implementing spectral preprocessing algorithms, designing appropriate neural network architectures (typically using feedforward networks with hidden layers), and optimizing regression parameters through backpropagation training. The process includes key steps such as spectral data normalization, feature extraction, model validation using techniques like cross-validation, and performance evaluation metrics like RMSE (Root Mean Square Error).

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