NAHL: a Neural network with an Augmented Hidden Layer

An interesting new architecture for artificial neural networks
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Updated Tue, 17 Jan 2023 01:13:53 +0000

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****The current version of NAHL is able to adapt with both classification and regession****
Please read these papers carefuly :
Please cite our NAHL papers as:
[1] T. Berghout, M. Benbouzid, S. M. Muyeen, T. Bentrcia, and L.-H. Mouss, “Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial Systems,” IEEE Access, vol. 9, pp. 152829–152840, 2021, doi: 10.1109/ACCESS.2021.3127084.
[2] T. Berghout and M. Benbouzid, “EL-NAHL: Exploring Labels Autoencoding in Augmented Hidden Layers of Feedforward Neural Networks for Cybersecurity in Smart Grids,” Reliab. Eng. Syst. Saf., p. 108680, Jun. 2022, doi: 10.1016/j.ress.2022.108680.
[3] T. Berghout, M. Benbouzid, Y. Amirat and G. Yao, "Lithium-ion Battery State of Health Prediction with a Robust Collaborative Augmented Hidden Layer Feedforward Neural Network Approach," in IEEE Transactions on Transportation Electrification, doi: 10.1109/TTE.2023.3237726.
MATLAB Release Compatibility
Created with R2018b
Compatible with any release
Platform Compatibility
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Auto_NAHL_codes

Version Published Release Notes
2.4.0

adding more references

2.3.0

New references have been added.

2.2.0

New published papers references has been added.

2.1.0

-New activation function ReLU
-Tolerance problem fixed
-Fitness function is replaced with RMSE loss function
-Adapting with both regression and classification

2.0.0

New activation function ReLU
Tolerance problem fixed
Fitness function is replaced with RMSE loss function
Adapting with both regression and classification

1.1.0

Citation is updated

1.0.0