Artificial Neural Networks-Based Energy Storage Predictor of (Ba0.85Ca0.15) (Ti0.9Zr0.1) O3 under Temperature-Induced Variation

Comprehending and predicting the fluctuations in the energy storage functionality of ferroelectric-based apparatuses throughout a broad range of temperatures is crucial. To achieve this, we developed and simulated a Function Fitting ANN model using MATLAB. The model was trained using the back-propag...

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Hlavní autoři:	Dina A. Naser, Mohamed Essai, Abd El-Razek Mahmoud, G. A. Gamal
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	South Valley University, Faculty of Engineering 2025-06-01
Edice:	SVU-International Journal of Engineering Sciences and Applications
Témata:	Artificial Neural Networks Energy Storage Parameters Ceramic Capacitors Temperature Variation Ferroelectric Hysteresis Loops
On-line přístup:	https://svusrc.journals.ekb.eg/article_389625_32074bbb71c3d202c3122d2e28b6056b.pdf
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