Explainable Neural Network Regression Model with SHAP

版本 1.0.1 (495.8 KB) 作者: Mita
Radial Basis Function Neural Network training include 5-fold cross-validation and SHAP analysis for explainable model
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更新时间 2024/12/16

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This MATLAB script implements an explainable neural network regression model using a Radial Basis Function Neural Network (RBFNN) to predict water flux in forward osmosis processes. The model utilizes operational parameters such as membrane area, feed and draw solution flow rates, and concentrations as input features for training. To enhance interpretability, SHapley Additive exPlanations (SHAP) are applied, allowing users to gain insights into the contribution of each parameter to the model's predictions. This tool provides a powerful solution for researchers and engineers looking to develop accurate and transparent regression models while leveraging the flexibility of RBFNNs for optimizing forward osmosis system performance.

引用格式

Mita (2025). Explainable Neural Network Regression Model with SHAP (https://ww2.mathworks.cn/matlabcentral/fileexchange/174170-explainable-neural-network-regression-model-with-shap), MATLAB Central File Exchange. 检索时间: .

MATLAB 版本兼容性
创建方式 R2024a
兼容 R2024a 到 R2024b 的版本
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版本 已发布 发行说明
1.0.1

The published script cannot run properly on the matlab version lower than R2024a
1.0.0