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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Kumar, Raj
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (13/13 displayed)
- 2024Wide-angle and polarization-insensitive perfect metamaterial absorbercitations
- 2023Control of skyrmion chirality in Ta/FeCoB/TaO$_x$ trilayers by TaO$_x$ oxidation and FeCoB thicknesscitations
- 2023Control of skyrmion chirality in Ta/FeCoB/TaO$_x$ trilayers by TaO$_x$ oxidation and FeCoB thicknesscitations
- 2023Tuning of Structural and Morphological Characteristics of V<sub>2</sub>O<sub>5</sub> Thin Films Using Low Energy 16 keV N + for Optical and Wetting Applicationscitations
- 2023Performance analysis of various training algorithms of deep learning based controllercitations
- 2022Investigation on Mechanical Durability Properties of High-Performance Concrete with Nanosilica and Copper Slagcitations
- 2022Platinum on Oxidized Graphene Sheets: A Bifunctional Electrocatalyst for Hydrogen Oxidation Reaction and Methanol Oxidation Reactioncitations
- 2021Impact of post annealing and hydrogen implantation on functional properties of Cu2O thin films for photovoltaic applicationscitations
- 2020Drying kinetics and acoustic properties of soft porous polymer materialscitations
- 2020Detection of crack in plywood using digital holography interferometry
- 2020Storage moduli and porosity of soft PDMS polyMIPEs can be controlled independently using thiol-ene click chemistrycitations
- 2019Nitrogen-Doped Cu2O Thin Films for Photovoltaic Applicationscitations
- 2019Settlement Analysis of Recycled Concrete Fine Aggregate Blended Soils using Geostudiocitations
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article
Performance analysis of various training algorithms of deep learning based controller
Abstract
<jats:title>Abstract</jats:title><jats:p>Advances in artificial neural networks (ANN), specifically deep learning (DL), have widened the application domain of process control. DL algorithms and models have become quite common these days. The training algorithm is the most important part of an ANN that affects the performance of the controller. Training algorithms optimize the weights and biases of the ANN according to the input-output patterns. In this paper, the performance of different training algorithms was evaluated, analysed, and compared in a feed-forward backpropagation architecture. The training algorithms were simulated on MATLAB R2021b with license number 1075356. Training data were generated using two benchmark problems of the process control system. The performance, gradient, training error, validation error, testing error, and regression of the different training algorithms were obtained and analysed. The data shows that the Levenberg-Marquardt (LM) algorithm produced the best validation performance with a value of 2.669*10<jats:sup>−14</jats:sup> at 2000 epochs, while ‘traingd’ and ‘traingdm’ algorithms did not improve beyond their initial values. The LM algorithm tends to produce better results than other algorithms. These results indicate that the LM backpropagation best suits these types of benchmark problems. The results also suggest that the choice of training algorithm can significantly impact the performance of a neural network.</jats:p>