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Journal articles on the topic 'Artificial Neural Network-based modeling'

Author: Grafiati

Published: 11 March 2023

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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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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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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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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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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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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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Salmia , M., Fridja , D., Baci , A. Bella, and Al-Douri , Y. "MOSFETs Modeling Using Artificial Neural Network." Journal of New Technology and Materials 8, no. 2 (2018): 55–58. http://dx.doi.org/10.12816/0053502.

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12

Priya, R., and Dr R. Mallika. "Ground Water Quality Modelling Using Data Mining Techniques and Artificial Neural Network Based Approach." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (2019): 1001–7. http://dx.doi.org/10.5373/jardcs/v11sp10/20192897.

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13

Wang, Jing, Yo-Han Kim, Jisu Ryu, Changwook Jeong, Woosung Choi, and Daesin Kim. "Artificial Neural Network-Based Compact Modeling Methodology for Advanced Transistors." IEEE Transactions on Electron Devices 68, no. 3 (2021): 1318–25. http://dx.doi.org/10.1109/ted.2020.3048918.

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14

Yang, Shaozeng, and Jianhua Zhang. "Artificial Neural Network based Predictive Modeling of Operator Functional State." IFAC Proceedings Volumes 46, no. 13 (2013): 371–76. http://dx.doi.org/10.3182/20130708-3-cn-2036.00005.

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15

Lee, Jong-Pil, and Sung-Soo Kim. "Static Load Modeling Based on Artificial Neural Network and Harmonics." Transactions of the Korean Institute of Electrical Engineers P 62, no. 2 (2013): 65–71. http://dx.doi.org/10.5370/kieep.2013.62.2.065.

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16

Lauret, Pierre, Frédéric Heymes, Laurent Aprin, and Anne Johannet. "Atmospheric dispersion modeling using Artificial Neural Network based cellular automata." Environmental Modelling & Software 85 (November 2016): 56–69. http://dx.doi.org/10.1016/j.envsoft.2016.08.001.

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17

Yassar, Reza S., Osama AbuOmar, Eric Hansen, and Mark F. Horstemeyer. "On dislocation-based artificial neural network modeling of flow stress." Materials & Design 31, no. 8 (2010): 3683–89. http://dx.doi.org/10.1016/j.matdes.2010.02.051.

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18

Ali Akcayol, M., and Can Cinar. "Artificial neural network based modeling of heated catalytic converter performance." Applied Thermal Engineering 25, no. 14-15 (2005): 2341–50. http://dx.doi.org/10.1016/j.applthermaleng.2004.12.014.

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19

Liu, Xiangdong, and Chunbo Xiu. "Hysteresis modeling based on the hysteretic chaotic neural network." Neural Computing and Applications 17, no. 5-6 (2007): 579–83. http://dx.doi.org/10.1007/s00521-007-0157-z.

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20

Singh, Gyanendra, Mahesh Pal, Yogender Yadav, and Tushar Singla. "Deep neural network-based predictive modeling of road accidents." Neural Computing and Applications 32, no. 16 (2020): 12417–26. http://dx.doi.org/10.1007/s00521-019-04695-8.

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21

SONG, YANGPO, and XIAOQI PENG. "MODELING METHOD USING COMBINED ARTIFICIAL NEURAL NETWORK." International Journal of Computational Intelligence and Applications 10, no. 02 (2011): 189–98. http://dx.doi.org/10.1142/s1469026811003057.

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To improve the modeling performance — such as accuracy and robustness — of artificial neural network (ANN), a new combined ANN and corresponding optimal modeling method are proposed in this paper. The combined ANN consists of two parallel sub-networks, and methods such as "early stopping" and "data resampling" are jointly used in training process to reduce the sensitivity of the modeling performance to its structure. To achieve better performance, the structure of combined ANN is proposed to be adjusted dynamically according to the information of expectation error and real error. Simulation ex

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22

Mohamed, R. A., and D. M. Habashy. "Thermal Conductivity Modeling of Propylene Glycol - Based Nanofluid Using Artificial Neural Network." JOURNAL OF ADVANCES IN PHYSICS 14, no. 1 (2018): 5281–91. http://dx.doi.org/10.24297/jap.v14i1.7177.

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The article introduces artificial neural network model that simulates and predicts thermal conductivity and particle size of propylene glycol - based nanofluids containing Al2O3 and TiO2 nanoparticles in a temperature rang 20 - 80oc. The experimental data indicated that the nanofluids have excellent stability over the temperature scale of interest and thermal conductivity enhancement for both nanofluid samples. The neural network system was trained on the available experimental data. The system was designed to find the optimal network that has the best training performance. The nonlinear equat

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23

Li, Xiuping, and Jianjun Gao. "PAD MODELING BY USING ARTIFICIAL NEURAL NETWORK." Progress In Electromagnetics Research 74 (2007): 167–80. http://dx.doi.org/10.2528/pier07041201.

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24

Mombeini, Hossein, and Abdolreza Yazdani-Chamzini. "Modeling Gold Price via Artificial Neural Network." Journal of Economics, Business and Management 3, no. 7 (2015): 699–703. http://dx.doi.org/10.7763/joebm.2015.v3.269.

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25

Khorshid, Motaz, Assem Tharwat, Amer Bader, and Ahmed Omran. "The ARIMA versus Artificial Neural Network Modeling." IJCI. International Journal of Computers and Information 2, no. 1 (2009): 30–40. http://dx.doi.org/10.21608/ijci.2009.33936.

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26

Iqbal, Asif. "Modeling Milling Process Using Artificial Neural Network." Advanced Materials Research 628 (December 2012): 128–34. http://dx.doi.org/10.4028/www.scientific.net/amr.628.128.

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Machining processes, such as milling, are considered to be too complex to be modeled accurately by using analytical or even numeric means due to involvement of various control parameters, some of them highly vague and imprecise. Such situation calls for application of nonconventional methods for modeling the responses of interest with acceptable degree of accuracy. In this work, a computational intelligence tool, possessing quick learning ability, has been used for modeling and predicting tool’s flank wear and workpiece surface roughness in milling of cold work tool steel. Six numeric and two

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27

Djavanroodi, F., B. Omranpour, and M. Sedighi. "Artificial Neural Network Modeling of ECAP Process." Materials and Manufacturing Processes 28, no. 3 (2013): 276–81. http://dx.doi.org/10.1080/10426914.2012.667889.

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28

Wang, Jun, Hai Li Zhang, and Zhang Yue Su. "Manufacturing Knowledge Modeling Based on Artificial Neural Network for Intelligent CAPP." Applied Mechanics and Materials 127 (October 2011): 310–15. http://dx.doi.org/10.4028/www.scientific.net/amm.127.310.

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CAPP(computer aided process planning) is a key technology for integration of CAD and CAM. Process planning relies on manufacturing knowledge and CAPP is characterized with multi-knowledge resources, multi-task, multi-level and multi-constrain so process planning is hard to automate. This paper introduces the artificial neural network for unstructured manufacturing knowledge modeling, knowledge is represented as neural network weight value matrix, and then form ANN database to support intelligent CAPP. Example about cutting force modification is presented to test the feasibility of this approac

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29

Ma, Yanying, Qiang Liu, Bohua Sun, Xiuzhen Li, and Ying Liu. "Wireless Sensor Modeling Optimization Algorithm Based on Artificial Intelligence Neural Network." Mobile Information Systems 2022 (July 30, 2022): 1–13. http://dx.doi.org/10.1155/2022/5296543.

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With the development and progress of society, science and technology have entered the field of view of scholars at home and abroad. As the science and technology with the biggest potential in recent years, wireless sensor network has been involved in many scientific fields. This paper aims to study the wireless sensor modeling optimization algorithm of artificial intelligence neural network. In this paper, a WSN data fusion algorithm (DFRMP) based on regression model prediction is proposed, and the four algorithms of SLR, PAQ, TINA, and DFRMP are compared. The experimental results of this pape

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30

Zhang, Yi, Hong Chen, Bingqiao Yang, Shupei Fu, Jie Yu, and Ziyue Wang. "Prediction of phosphate concentrate grade based on artificial neural network modeling." Results in Physics 11 (December 2018): 625–28. http://dx.doi.org/10.1016/j.rinp.2018.10.011.

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31

Falahian, Razieh, Maryam Mehdizadeh Dastjerdi, Malihe Molaie, Sajad Jafari, and Shahriar Gharibzadeh. "Artificial neural network-based modeling of brain response to flicker light." Nonlinear Dynamics 81, no. 4 (2015): 1951–67. http://dx.doi.org/10.1007/s11071-015-2118-x.

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32

Salari, D., and K. Rostamizadeh. "Oxidative Desulfurization of Fuel Oil: Modeling Based on Artificial Neural Network." Petroleum Science and Technology 26, no. 4 (2008): 382–97. http://dx.doi.org/10.1080/10916460600809592.

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33

Trichakis, Ioannis C., Ioannis K. Nikolos, and G. P. Karatzas. "Artificial Neural Network (ANN) Based Modeling for Karstic Groundwater Level Simulation." Water Resources Management 25, no. 4 (2010): 1143–52. http://dx.doi.org/10.1007/s11269-010-9628-6.

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34

Watson, P. M., K. C. Gupta, and R. L. Mahajan. "Applications of knowledge-based artificial neural network modeling to microwave components." International Journal of RF and Microwave Computer-Aided Engineering 9, no. 3 (1999): 254–60. http://dx.doi.org/10.1002/(sici)1099-047x(199905)9:3<254::aid-mmce9>3.0.co;2-g.

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35

Ruciński, Dariusz. "The Influence of the Artificial Neural Network type on the quality of learning on the Day-Ahead Market model at Polish Power Exchange joint-stock company." Studia Informatica, no. 23 (December 22, 2020): 77–93. http://dx.doi.org/10.34739/si.2019.23.05.

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The work contains the results of the Day-Ahead Market modeling research at Polish Power Exchange taking into account the numerical data on the supplied and sold electricity in selected time intervals from the entire period of its operation (from July 2002 to June 2019). Market modeling was carried out based on three Artificial Neural Network models, ie: Perceptron Artificial Neural Network, Recursive Artificial Neural Network, and Radial Artificial Neural Network. The examined period of the Day-Ahead Market operation on the Polish Power Exchange was divided into sub-periods of various lengths,

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36

Turovskii, Ya A., E. V. Bogatikov, S. G. Tikhomirov, and A. A. Adamenko. "Modeling the restoration of biological and biotechnical systems using hardware analog and software artificial neural networks." Proceedings of the Voronezh State University of Engineering Technologies 80, no. 2 (2018): 86–92. http://dx.doi.org/10.20914/2310-1202-2018-2-86-92.

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A hardware analog model of an artificial neural network was developed, based on a specially trained software artificial neural network, for modeling the process of recovering damaged biological and biotechnical systems using neurochips based on the evolutionary method of training. A series of 12 computational experiments on the restoration of a damaged hardware analog artificial neural network with the help of a software artificial neural network was carried out. To restore a damaged network, an evolutionary approach is used. In most cases, it is possible to restore a damaged hardware analog n

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Huang, Haocai, Bofu Zheng, Yihong Wang, and Yan Wei. "Wavelet Neural Network for Modeling Chlorophyll a Concentration Affected by Artificial Upwelling." Mathematical Problems in Engineering 2019 (October 30, 2019): 1–9. http://dx.doi.org/10.1155/2019/4590981.

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Through bringing nutrient-rich subsurface water to the surface, the artificial upwelling technology is applied to increase the primary marine productivity which could be assessed by Chlorophyll a concentration. Chlorophyll a concentration may vary with different water physical properties. Therefore, it is necessary to study the relationship between Chlorophyll a concentration and other water physical parameters. To ensure the accuracy of predicting the concentration of Chlorophyll a, we develop several models based on wavelet neural network (WNN). In this study, we build up a three-layer basic

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38

Parmar, H., and D. A. Hindoliya. "Artificial neural network based modelling of desiccant wheel." Energy and Buildings 43, no. 12 (2011): 3505–13. http://dx.doi.org/10.1016/j.enbuild.2011.09.016.

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39

Daheb, Kahina, Mark L. Lipman, Patrice Hildgen, and Julie J. Roy. "Artificial Neural Network Modeling for Drug Dialyzability Prediction." Journal of Pharmacy & Pharmaceutical Sciences 16, no. 5 (2013): 665. http://dx.doi.org/10.18433/j35c8b.

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Purpose. The purpose of this study was to develop an artificial neural network (ANN) model to predict drug removal during dialysis based on drug properties and dialysis conditions. Nine antihypertensive drugs were chosen as model for this study. Methods. Drugs were dissolved in a physiologic buffer and dialysed in vitro in different dialysis conditions (UFRmin/UFRmax, with/without BSA). Samples were taken at regular intervals and frozen at -20ºC until analysis. Extraction methods were developed for drugs that were dialysed with BSA in the buffer. Drug concentrations were quantified by high per

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40

Zakaria, Mamang, Luther Pagiling, and Wa Ode Siti Nur Alam. "Sistem Penyiraman Otomatis Tanaman Semusim Berbasis Jaringan Saraf Tiruan Multilayer Perceptron." Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) 7, no. 1 (2022): 35. http://dx.doi.org/10.33772/jfe.v7i1.24050.

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In general, farmers water plants when the conditions are met, such as dry soil, no rain, and cold temperatures. One of the efficient ways to control it is to use an artificial neural network-based automatic plant watering system. The purpose of this study was to determine the success of artificial neural networks as decision-makers to water plants automatically. The stages of designing an automatic watering system based on an artificial neural network were to build software including artificial neural network modeling and Arduino microcontroller programming, automatically watering tools, evalu

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41

Pavić, Ivica, Frano Tomašević, and Ivana Damjanović. "Application of artificial neural networks for external network equivalent modeling." Journal of Energy - Energija 64, no. 1-4 (2022): 275–84. http://dx.doi.org/10.37798/2015641-4156.

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In this paper an artificial neural network (ANN) based methodology is proposed for determining an external network equivalent. The modified Newton-Raphson method with constant interchange of total active power between internal and external system is used for solving the load flow problem. A multilayer perceptron (MLP) with backpropagation training algorithm is applied for external network determination. The proposed methodology was tested with the IEEE 24-bus test network and simulation results show a very good performance of the ANN for external network modeling.

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42

Scott, Gary M., and W. Harmon Ray. "Neural Network Process Models Based on Linear Model Structures." Neural Computation 6, no. 4 (1994): 718–38. http://dx.doi.org/10.1162/neco.1994.6.4.718.

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The KBANN (Knowledge-Based Artificial Neural Networks) approach uses neural networks to refine knowledge that can be written in the form of simple propositional rules. This idea is extended by presenting the MANNIDENT (Multivariable Artificial Neural Network Identification) algorithm by which the mathematical equations of linear dynamic process models determine the topology and initial weights of a network, which is further trained using backpropagation. This method is applied to the task of modeling a nonisothermal chemical reactor in which a first-order exothermic reaction is occurring. This

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43

Du, Xin Hui, Shu Niu, Xing Min Wang, and Pu Hui Wang. "Study on the Modeling of Main Mine Ventilator Based on Artificial Intelligence." Applied Mechanics and Materials 130-134 (October 2011): 3526–30. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.3526.

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The parameters of main ventilator in mine such as air flow, wind speed, gas concentration and other conditions are closely related, for its complexity, it’s difficult to establish the nonlinear mathematic model, and it’s hard describe the model properties by traditional identification method. Neural network and Fuzzy system are used in mine main ventilator model identification. A Neural network based on RBF is used in neural network, and a T-S fuzzy model based on triangle membership function is used in Fuzzy identification. The simulation results show that the two methods can satisfy the need

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44

Chayjan, R. A., and M. Esna-Ashari. "Modeling of heat and entropy sorption of maize (cv. Sc704): neural network method." Research in Agricultural Engineering 56, No. 2 (2010): 69–76. http://dx.doi.org/10.17221/37/2009-rae.

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Equilibrium moisture content of maize affects its values of dehydration heat and entropy. Precise prediction of heat and entropy with regard to its equilibrium moisture content is a simple and fast method for proper estimation of energy required for dehydration of maize and simulation of dried maize storage. Artificial neural network and thermodynamic equations for computation of maize heat and entropy of sorption were used, as a new method. The artificial neural network method for prediction of the equilibrium moisture content of maize was utilized. The heat of sorption of maize is predicted

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45

SINGH, AMAR PARTAP, TARA SINGH KAMAL, and SHAKTI KUMAR. "ARTIFICIAL NEURAL NETWORK BASED SOFT ESTIMATOR FOR ESTIMATION OF TRANSDUCER STATIC NONLINEARITY." International Journal of Neural Systems 14, no. 04 (2004): 237–46. http://dx.doi.org/10.1142/s0129065704001991.

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In this work, the development of an Artificial Neural Network (ANN) based soft estimator is reported for the estimation of static-nonlinearity associated with the transducers. Under the realm of ANN based transducer modeling, only two neural models have been suggested to estimate the static-nonlinearity associated with the transducers with quite successful results. The first existing model is based on the concept of a functional link artificial neural network (FLANN) trained with μ-LMS (Least Mean Squares) learning algorithm. The second one is based on the architecture of a single layer linear

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Zhu, Yu Hua, and Dian Zheng Zhuang. "Research of the Steady-State Modeling Method Based on Neural Network." Applied Mechanics and Materials 441 (December 2013): 526–29. http://dx.doi.org/10.4028/www.scientific.net/amm.441.526.

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Nitric acid production process is complicated reaction mechanism, serious non-linear the traditional mechanism modeling to get the low accuracy of the mathematical model. This paper adopts a non-mechanism modeling, using the production history data, the use of artificial neural network has the right to arbitrary nonlinear mapping any approximation ability to simulate the relationship of the actual system input-output, trained to be get steady-state model of the nitric acid production process.

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Zhang, Kun, Zhen Zhang, Yuning Han, Yinggang Gu, Qinggang Qiu, and Xiaojing Zhu. "Artificial neural network modeling for steam ejector design." Applied Thermal Engineering 204 (March 2022): 117939. http://dx.doi.org/10.1016/j.applthermaleng.2021.117939.

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48

Derogar, Ali, and Faramarz Djavanroodi. "Artificial Neural Network Modeling of Forming Limit Diagram." Materials and Manufacturing Processes 26, no. 11 (2011): 1415–22. http://dx.doi.org/10.1080/10426914.2010.544818.

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49

Andrawis, Robert R., Mohamed A. Swillam, Mohamed A. El-Gamal, and Ezzeldin A. Soliman. "Artificial neural network modeling of plasmonic transmission lines." Applied Optics 55, no. 10 (2016): 2780. http://dx.doi.org/10.1364/ao.55.002780.

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50

GORUCU, F. B. "Artificial Neural Network Modeling for Forecasting Gas Consumption." Energy Sources 26, no. 3 (2004): 299–307. http://dx.doi.org/10.1080/00908310490256626.

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