Optimal Architecture Deep Neural Network for Regression

These codes perform optimal architecture deep neural network model generation for regression problems and illustrate e-nose application.
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Updated 27 Mar 2024

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Deep Neural Network (DNN) models require a suitable neural architecture (topology) for data driven modeling. Avoiding overfitting or underfitting problems and achieving a satisfactory generalization are main concerns in modeling tasks. To deal with these concerns, optimization of neural architecture according to the dataset is an effective solution.
These codes perform optimization of deep neural network model architecture for regression problems. An example is shown for e-nose application.

Cite As

Simsek, Ozlem Imik, and Baris Baykant Alagoz. “Optimal Architecture Artificial Neural Network Model Design with Exploitative Alpha Gray Wolf Optimization for Soft Calibration of CO Concentration Measurements in Electronic Nose Applications.” Transactions of the Institute of Measurement and Control, vol. 45, no. 4, SAGE Publications, Sept. 2022, pp. 686–99, doi:10.1177/01423312221119648.

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Alagoz, Baris Baykant, et al. “An Evolutionary Field Theorem: Evolutionary Field Optimization in Training of Power-Weighted Multiplicative Neurons for Nitrogen Oxides-Sensitive Electronic Nose Applications.” Sensors, vol. 22, no. 10, MDPI AG, May 2022, p. 3836, doi:10.3390/s22103836.

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İMİK ŞİMŞEK, Özlem, and Barış Baykant ALAGÖZ. “Model Based Demand Order Estimation by Using Optimal Architecture Artificial Neural Network with Metaheuristic Optimizations.” Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 12, no. 3, Igdir University, Sept. 2022, pp. 1277–91, doi:10.21597/jist.1099154.

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MATLAB Release Compatibility
Created with R2014b
Compatible with any release
Platform Compatibility
Windows macOS Linux
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Version Published Release Notes
1.0.3

Some texts were improved in scripts.

1.0.2

Document was improved.

1.0.1

Readme file is improved.

1.0.0