PLS Soft Sensor via NIPALS Algorithm

MATLAB implementation of Partial Least Squares (PLS) regression using the NIPALS algorithm for soft sensor modeling and prediction.

Overview

This package provides a MATLAB implementation of a Soft Sensor based on Partial Least Squares (PLS) regression. The core of the model utilizes the Non-linear Iterative Partial Least Squares (NIPALS) algorithm to efficiently extract latent variables and compute loadings and weights. It is highly suitable for industrial process data modeling.

File Structure

1. main_soft_sensor.m - The main script to load data, preprocess, train the model, and evaluate performance.
2. NIPALS.m - The core function that performs the NIPALS iterative decomposition.
3. pls_predict.m - Function to make predictions on test data using the trained PLS model.
4. r2_score.m - Function to calculate the R-squared metric.
5. RMSE_indices.m - Function to calculate the Root Mean Squared Error.
6. drawing.m - Visualization function to plot the results.

Usage

Simply place all the .m files and your dataset (e.g., te_data.csv) in the same directory. Run the main_soft_sensor.m script in MATLAB. You can adjust the number of latent variables (K) and the train/test split ratio directly within the main script.

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