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Добірка наукової літератури з теми "Artificial Neural Network-based modeling"

Автор: Grafiati

Опубліковано: 11 березня 2023

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Статті в журналах з теми "Artificial Neural Network-based modeling"

1

Zhang, Ji, Sheng Chang, Hao Wang, Jin He, and Qi Jun Huang. "Artificial Neural Network Based CNTFETs Modeling." Applied Mechanics and Materials 667 (October 2014): 390–95. http://dx.doi.org/10.4028/www.scientific.net/amm.667.390.

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Based on artificial neural network (ANN), a new method of modeling carbon nanotube field effect transistors (CNTFETs) is developed. This paper presents two ANN CNTFET models, including P-type CNTFET (PCNTFET) and N-type CNTFET (NCNTFET). In order to describe the devices more accurately, a segmentation voltage of the voltage between gate and source is defined for each type of CNTFET to segment the workspace of CNTFET. With the smooth connection by a quasi-Fermi function for, the two segmented networks of CNTFET are integrated into a whole device model and implemented by Verilog-A. To validate t

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2

Hiyama, T., M. Tokieda, W. Hubbi, and H. Andou. "Artificial neural network based dynamic load modeling." IEEE Transactions on Power Systems 12, no. 4 (1997): 1576–83. http://dx.doi.org/10.1109/59.627861.

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3

Wang, Jun, Feng Qin Yu, and Feng He Wu. "Cutting Data Modeling Based on Artificial Neural Network." Key Engineering Materials 620 (August 2014): 544–49. http://dx.doi.org/10.4028/www.scientific.net/kem.620.544.

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Cutting force is usually obtained based on the experimental data which is conducted under certain cutting condition with certain cutters because metal cutting mechanism study is not mature. As the data are numerous, in different types, and the relationships between them are complex, the commercial database can be used directly. A new approach based on ANN is introduced here for unstructured and discrete data modeling, which transfers the unstructured and discrete data into ANN topology and net weight matrix. In this paper, the experimental data of union cutting force modification are taken as

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4

Faghri, Ardeshir, and Sandeep Aneja. "Artificial Neural Network–Based Approach to Modeling Trip Production." Transportation Research Record: Journal of the Transportation Research Board 1556, no. 1 (1996): 131–36. http://dx.doi.org/10.1177/0361198196155600115.

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Accurate and reliable estimates of trip production of a study area are important for an accurate forecast from the four-step travel demand forecasting procedure. In the trip generation step, trip production estimates are considered more accurate, and trip attractions are adjusted while keeping the productions constant. This means that more accurate trip production rates will result in more reliable forecasts. Improving the accuracy of forecasts requires an extensive and reliable data base or improvement in the modeling techniques. Since data base enhancement is costly and time-consuming, an al

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5

Longfei, Tang, Xu Zhihong, and Bala Venkatesh. "Contactor Modeling Technology Based on an Artificial Neural Network." IEEE Transactions on Magnetics 54, no. 2 (2018): 1–8. http://dx.doi.org/10.1109/tmag.2017.2767555.

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6

Panahi, Shirin, Zainab Aram, Sajad Jafari, Jun Ma, and J. C. Sprott. "Modeling of epilepsy based on chaotic artificial neural network." Chaos, Solitons & Fractals 105 (December 2017): 150–56. http://dx.doi.org/10.1016/j.chaos.2017.10.028.

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7

Rai, Raveendra K., and B. S. Mathur. "Event-based Sediment Yield Modeling using Artificial Neural Network." Water Resources Management 22, no. 4 (2007): 423–41. http://dx.doi.org/10.1007/s11269-007-9170-3.

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8

Xie, Shuai, Wenyan Wu, Sebastian Mooser, Q. J. Wang, Rory Nathan, and Yuefei Huang. "Artificial neural network based hybrid modeling approach for flood inundation modeling." Journal of Hydrology 592 (January 2021): 125605. http://dx.doi.org/10.1016/j.jhydrol.2020.125605.

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9

HASEENA, H., PAUL K. JOSEPH, and ABRAHAM T. MATHEW. "ARTIFICIAL NEURAL NETWORK BASED ECG ARRHYTHMIA CLASSIFICATION." Journal of Mechanics in Medicine and Biology 09, no. 04 (2009): 507–25. http://dx.doi.org/10.1142/s0219519409003103.

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Reliable and computationally efficient means of classifying electrocardiogram (ECG) signals has been the subject of considerable research effort in recent years. This paper explores the potential applications of a talented, versatile computation model called the Artificial Neural Network (ANN) in the field of ECG signal classification. Two types of ANNs: Multi-Layered Feed Forward Network (MLFFN) and Probabilistic Neural Networks (PNN) are used to classify seven types of ECG beats. It includes six types of arrhythmia data and normal data. Here, parametric modeling strategies are used in conjun

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10

Çelik, Şenol. "MODELING AVOCADO PRODUCTION IN MEXICO WITH ARTIFICIAL NEURAL NETWORKS." Engineering and Technology Journal 07, no. 10 (2022): 1605–9. http://dx.doi.org/10.47191/etj/v7i10.08.

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An Artificial Neural Network (ANN) model was created in this research to estimate and predict the amount of avocado production in Mexico. In the development of the ANN model, the years that are time variable were used as the input parameter, and the avocado production amount (tons) was used as the output parameter. The research data includes avocado production in Mexico for 1961-2020 period. Mean Squared Error (MSE) and Mean Absolut Error (MAE) statistics were calculated using hyperbolic tangent activation function to determine the appropriate model. ANN model is a network architecture with 12

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