Literatura académica sobre el tema "Artificial Neural Network-based modeling"
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Artículos de revistas sobre el tema "Artificial Neural Network-based modeling"
Zhang, Ji, Sheng Chang, Hao Wang, Jin He y Qi Jun Huang. "Artificial Neural Network Based CNTFETs Modeling". Applied Mechanics and Materials 667 (octubre de 2014): 390–95. http://dx.doi.org/10.4028/www.scientific.net/amm.667.390.
Texto completoHiyama, T., M. Tokieda, W. Hubbi y H. Andou. "Artificial neural network based dynamic load modeling". IEEE Transactions on Power Systems 12, n.º 4 (1997): 1576–83. http://dx.doi.org/10.1109/59.627861.
Texto completoWang, Jun, Feng Qin Yu y Feng He Wu. "Cutting Data Modeling Based on Artificial Neural Network". Key Engineering Materials 620 (agosto de 2014): 544–49. http://dx.doi.org/10.4028/www.scientific.net/kem.620.544.
Texto completoFaghri, Ardeshir y Sandeep Aneja. "Artificial Neural Network–Based Approach to Modeling Trip Production". Transportation Research Record: Journal of the Transportation Research Board 1556, n.º 1 (enero de 1996): 131–36. http://dx.doi.org/10.1177/0361198196155600115.
Texto completoLongfei, Tang, Xu Zhihong y Bala Venkatesh. "Contactor Modeling Technology Based on an Artificial Neural Network". IEEE Transactions on Magnetics 54, n.º 2 (febrero de 2018): 1–8. http://dx.doi.org/10.1109/tmag.2017.2767555.
Texto completoPanahi, Shirin, Zainab Aram, Sajad Jafari, Jun Ma y J. C. Sprott. "Modeling of epilepsy based on chaotic artificial neural network". Chaos, Solitons & Fractals 105 (diciembre de 2017): 150–56. http://dx.doi.org/10.1016/j.chaos.2017.10.028.
Texto completoRai, Raveendra K. y B. S. Mathur. "Event-based Sediment Yield Modeling using Artificial Neural Network". Water Resources Management 22, n.º 4 (4 de mayo de 2007): 423–41. http://dx.doi.org/10.1007/s11269-007-9170-3.
Texto completoXie, Shuai, Wenyan Wu, Sebastian Mooser, Q. J. Wang, Rory Nathan y Yuefei Huang. "Artificial neural network based hybrid modeling approach for flood inundation modeling". Journal of Hydrology 592 (enero de 2021): 125605. http://dx.doi.org/10.1016/j.jhydrol.2020.125605.
Texto completoHASEENA, H., PAUL K. JOSEPH y ABRAHAM T. MATHEW. "ARTIFICIAL NEURAL NETWORK BASED ECG ARRHYTHMIA CLASSIFICATION". Journal of Mechanics in Medicine and Biology 09, n.º 04 (diciembre de 2009): 507–25. http://dx.doi.org/10.1142/s0219519409003103.
Texto completoÇelik, Şenol. "MODELING AVOCADO PRODUCTION IN MEXICO WITH ARTIFICIAL NEURAL NETWORKS". Engineering and Technology Journal 07, n.º 10 (31 de octubre de 2022): 1605–9. http://dx.doi.org/10.47191/etj/v7i10.08.
Texto completoTesis sobre el tema "Artificial Neural Network-based modeling"
Brunger, Clifford A. "Artificial neural network modeling of damaged aircraft". Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1994. http://handle.dtic.mil/100.2/ADA283227.
Texto completoSaptoro, Agus. "An integrated approach to artificial neural network based process modelling". Thesis, Curtin University, 2010. http://hdl.handle.net/20.500.11937/2484.
Texto completoAjayi, Toluwaleke. "Modeling Discharge and Water Chemistry Using Artificial Neural Network". Ohio University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1620167556121952.
Texto completoRothrock, Ling. "Modeling skilled decision-making using artificial neural network and genetic-based machine learning techniques". Thesis, Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/25084.
Texto completoByrne, Brian James. "An evaluation of artificial neural network modeling for manpower analysis". Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA273001.
Texto completoThesis advisor(s): George W. Thomas ; Timothy P. Hill. "September 1993." Includes bibliographical references. Also available online,
FALCIONELLI, NICOLA. "From Symbolic Artificial Intelligence to Neural Networks Universality with Event-based Modeling". Doctoral thesis, Università Politecnica delle Marche, 2020. http://hdl.handle.net/11566/274620.
Texto completoRepresenting knowledge, modeling human reasoning, and understanding thought processes have always been central parts of intellectual activities, since the first attempts by greek philosophers. It is not just by chance that, as soon as computers started to spread, remarkable scientists and mathematicians such as John McCarthy, Marvin Minsky and Claude Shannon started creating Artificially Intelligent systems with a symbolic oriented perspective. Even though this has been a partially forced path due to the very limited computing capabilities at the time, it marked the beginning of what is now known as Classical (or Symbolic) Artificial Intelligence, or essentially, a set of techniques for implementing "intelligent" behaviours by means of logic formalisms and theorem proving. Classical AI techniques are indeed very direct and human-centered processes, which find their strenghts on straightforward human interpretability and knowledge reusability. On the contrary, they suffer of computability problems when applied to real world tasks, mostly due to search space combinatorial explosion (especially when reasoning with time), and undecidability. However, the ever-increasing capabilites of computer hardware opened new possibilities for other more statistical-oriented methods to grow, such as Neural Networks. Even if the theory behind these methods was long known, it was only in recent years that they managed to achieve significant breakthroughs, and to surpass Classical AI techniques on many tasks. At the moment, the main hurdles of such statistical AI techniques are represented by the high energy consumption and the lack of easy ways for humans to understand the process that led to a particular result. Summing up, Classical and Statistical AI techniques can be seen as two faces of the same coin: if a domain presents structured information, little uncertainty, and clear decision processes, then Classical AI might be the right tool, or otherwise, when the information is less structured, has more uncertainty, ambiguity and clear decision processes cannot be identified, then Statistical AI should be chosen. The main purpose of this thesis is thus (i) to show capabilities and limits of current (Classical and Statistical) Artificial Intelligence techniques in both structured and unstructured domains, and (ii) to demostrate how event-based modeling can tackle some of their critical issues, providing new potential connections and novel perspectives.
FLECK, JULIA LIMA. "ARTIFICIAL NEURAL NETWORK MODELING FOR QUALITY INFERENCE OF A POLYMERIZATION PROCESS". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2008. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=12980@1.
Texto completoO presente trabalho apresenta o desenvolvimento de um modelo neural para a inferência da qualidade do polietileno de baixa densidade (PEBD) a partir dos valores das variáveis de processo do sistema reacional. Para tal, fez- se uso de dados operacionais de uma empresa petroquímica, cujo pré-processamento incluiu a seleção de variáveis, limpeza e normalização dos dados selecionados e preparação dos padrões. A capacidade de inferência do modelo neural desenvolvido neste estudo foi comparada com a de dois modelos fenomenológicos existentes. Para tal, utilizou-se como medida de desempenho o valor do erro médio absoluto percentual dos modelos, tendo como referência valores experimentais do índice de fluidez. Neste contexto, o modelo neural apresentou-se como uma eficiente ferramenta de modelagem da qualidade do sistema reacional de produção do PEBD.
This work comprises the development of a neural network- based model for quality inference of low density polyethylene (LDPE). Plant data corresponding to the process variables of a petrochemical company`s LDPE reactor were used for model development. The data were preprocessed in the following manner: first, the most relevant process variables were selected, then data were conditioned and normalized. The neural network- based model was able to accurately predict the value of the polymer melt index as a function of the process variables. This model`s performance was compared with that of two mechanistic models developed from first principles. The comparison was made through the models` mean absolute percentage error, which was calculated with respect to experimental values of the melt index. The results obtained confirm the neural network model`s ability to infer values of quality-related measurements of the LDPE reactor.
Li, Tan. "Tire-Pavement Interaction Noise (TPIN) Modeling Using Artificial Neural Network (ANN)". Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/87417.
Texto completoPHD
Bhanot, Nishant. "Artificial Neural Networks based Modeling and Analysis of Semi-Active Damper System". Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78295.
Texto completoMaster of Science
Kvale, David Thomas. "Artificial Neural Network-Based Approaches for Modeling the Radiated Emissions from Printed Circuit Board Structures and Shields". University of Toledo / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1280698960.
Texto completoLibros sobre el tema "Artificial Neural Network-based modeling"
Artificial neural network modeling of water and wastewater treatment processes. Hauppauge, N.Y: Nova Science Publishers, 2010.
Buscar texto completoKhataee, A. R. Artificial neural network modeling of water and wastewater treatment processes. Hauppauge, N.Y: Nova Science Publishers, 2010.
Buscar texto completoGuan, Biing T. Modeling training site vegetation coverage probability with a random optimization procedure: An artificial neural network approach. [Champaign, IL]: US Army Corps of Engineers, Construction Engineering Research Laboratories, 1998.
Buscar texto completoShanmuganathan, Subana y Sandhya Samarasinghe, eds. Artificial Neural Network Modelling. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28495-8.
Texto completoS, Mohan. Artificial neural network modelling. Roorkee: Indian National Committee on Hydrology, 2007.
Buscar texto completoNational Hydrology Symposium (4th 1993 Cardiff, Wales). Rainfall-runoff modeling as a problem in artificial intelligence: experience with a neural network. Fourth National Hydrology Symposium: (held at) University of Wales College of Cardiff 13-16th September 1993. (London?): British Hydrological Society, 1993.
Buscar texto completoThrun, Sebastian. Explanation-Based Neural Network Learning: A Lifelong Learning Approach. Boston, MA: Springer US, 1996.
Buscar texto completoThrun, Sebastian. Explanation-based neural network learning: A lifelong learning approach. Boston: Kluwer Academic Publishers, 1996.
Buscar texto completoWhite, Roger. The artificial intelligence of urban dynamics: Neural network modelling of urban structure. [Toronto]: Centre for Urban and Community Studies, University of Toronto, 1989.
Buscar texto completoDaniel, Sarit. Wavelet based artificial neural network and entropy detection techniques for a chaosmaker. Ottawa: National Library of Canada, 2002.
Buscar texto completoCapítulos de libros sobre el tema "Artificial Neural Network-based modeling"
Božnar, Marija Zlata y Primož Mlakar. "Artificial Neural Network-Based Environmental Models". En Air Pollution Modeling and Its Application XIV, 483–92. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/0-306-47460-3_49.
Texto completoBelkhode, Pramod, Sarika Modak, Vinod Ganvir y Anand Shende. "Artificial Neural Network Simulation". En Mathematical Modeling and Simulation, 63–85. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003132127-7.
Texto completoRao, Ming, Qijun Xia y Yiqun Ying. "Modeling via Artificial Neural Network". En Modeling and Advanced Control for Process Industries, 245–63. London: Springer London, 1994. http://dx.doi.org/10.1007/978-1-4471-2094-0_9.
Texto completoXiong, Wangping, Jianqiang Du, Qinglong Shu y Yi Zhao. "Artificial Neural Network Based Modeling of Glucose Metabolism". En Advances in Computer Science, Intelligent System and Environment, 623–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23756-0_100.
Texto completoBataineh, Mohammad, Timothy Marler y Karim Abdel-Malek. "Artificial Neural Network-Based Prediction of Human Posture". En Digital Human Modeling and Applications in Health, Safety, Ergonomics, and Risk Management. Human Body Modeling and Ergonomics, 305–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39182-8_36.
Texto completoAziz, Kashif, Ataur Rahman y Asaad Shamseldin. "Development of Artificial Intelligence Based Regional Flood Estimation Techniques for Eastern Australia". En Artificial Neural Network Modelling, 307–23. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28495-8_13.
Texto completoSeptiawan, Reza, Arief Rufiyanto, Sardjono Trihatmo, Budi Sulistya, Erik Madyo Putro y Subana Shanmuganathan. "Artificial Neural Network (ANN) Pricing Model for Natural Rubber Products Based on Climate Dependencies". En Artificial Neural Network Modelling, 423–42. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28495-8_20.
Texto completoAl-Yousef, Ali y Sandhya Samarasinghe. "Improved Ultrasound Based Computer Aided Diagnosis System for Breast Cancer Incorporating a New Feature of Mass Central Regularity Degree (CRD)". En Artificial Neural Network Modelling, 213–33. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28495-8_10.
Texto completoLemetre, Christophe, Lee J. Lancashire, Robert C. Rees y Graham R. Ball. "Artificial Neural Network Based Algorithm for Biomolecular Interactions Modeling". En Lecture Notes in Computer Science, 877–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02478-8_110.
Texto completoDutta, Pijush, Souvik Pal, Asok Kumar y Korhan Cengiz. "A Practical Approach to Neural Network Models". En Artificial Intelligence for Cognitive Modeling, 43–72. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003216001-4.
Texto completoActas de conferencias sobre el tema "Artificial Neural Network-based modeling"
Sousa, Miguel Angelo de Abreu de y Thiago Felipe de Jesus Torres. "Modeling of Pain on a FPGA-based Neural Network". En Artificial Intelligence and Applications. Calgary,AB,Canada: ACTAPRESS, 2013. http://dx.doi.org/10.2316/p.2013.793-034.
Texto completoKaracor, Mevlut, Kadir Yilmaz y Feriha Erfan Kuyumcu. "Modeling MCSRM with artificial neural network". En 2007 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) and Electromotion '07. IEEE, 2007. http://dx.doi.org/10.1109/acemp.2007.4510569.
Texto completoHiyama, T., N. Suzuki, H. Karino, Kwang Yun Lee y H. Andou. "Artificial neural network based modeling of governor-turbine system". En IEEE Power Engineering Society. 1999 Winter Meeting (Cat. No.99CH36233). IEEE, 1999. http://dx.doi.org/10.1109/pesw.1999.747437.
Texto completoA . K., Prakash, Amol Patil y Kalyani U. "Artificial Neural Network Based Driver Modeling for Vehicle Systems". En 8th SAEINDIA International Mobility Conference & Exposition and Commercial Vehicle Engineering Congress 2013 (SIMCOMVEC). 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2013. http://dx.doi.org/10.4271/2013-01-2860.
Texto completoHuang, Jiarong, Guangqin Gao, Xianyu Meng y Yuxiu Guan. "Modeling stand density index based on artificial neural network". En 2010 Sixth International Conference on Natural Computation (ICNC). IEEE, 2010. http://dx.doi.org/10.1109/icnc.2010.5584350.
Texto completoGupta, Subham, Achyut Paudel, Mishal Thapa, Sameer B. Mulani y Robert Walters. "Adaptive Sampling-Based Artificial Neural Network for Surrogate Modeling". En AIAA SCITECH 2022 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2022. http://dx.doi.org/10.2514/6.2022-0805.
Texto completoYurshin, V. G. "Artificial Neural Network Architecture Tuning Algorithm". En International Workshop “Hybrid methods of modeling and optimization in complex systems”. European Publisher, 2023. http://dx.doi.org/10.15405/epct.23021.29.
Texto completoWang, Yongqing, Huawei Shen, Shenghua Liu, Jinhua Gao y Xueqi Cheng. "Cascade Dynamics Modeling with Attention-based Recurrent Neural Network". En Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/416.
Texto completoThoeurn, Muy, Ardyono Priyadi, Anang Tjahjono y Mauridhi Hery Purnomo. "Overcurrent relay modeling using artificial neural network". En 2017 International Electrical Engineering Congress (iEECON). IEEE, 2017. http://dx.doi.org/10.1109/ieecon.2017.8075794.
Texto completoSapounaki, Maria y Athanasios Kakarountas. "A High-Performance Neuron for Artificial Neural Network based on Izhikevich model". En 2019 29th International Symposium on Power and Timing Modeling, Optimization and Simulation (PATMOS). IEEE, 2019. http://dx.doi.org/10.1109/patmos.2019.8862154.
Texto completoInformes sobre el tema "Artificial Neural Network-based modeling"
Hsieh, Bernard B. y Charles L. Bartos. Riverflow/River Stage Prediction for Military Applications Using Artificial Neural Network Modeling. Fort Belvoir, VA: Defense Technical Information Center, agosto de 2000. http://dx.doi.org/10.21236/ada382991.
Texto completoMarkova, Oksana, Serhiy Semerikov y Maiia Popel. СoCalc as a Learning Tool for Neural Network Simulation in the Special Course “Foundations of Mathematic Informatics”. Sun SITE Central Europe, mayo de 2018. http://dx.doi.org/10.31812/0564/2250.
Texto completoYaroshchuk, Svitlana O., Nonna N. Shapovalova, Andrii M. Striuk, Olena H. Rybalchenko, Iryna O. Dotsenko y Svitlana V. Bilashenko. Credit scoring model for microfinance organizations. [б. в.], febrero de 2020. http://dx.doi.org/10.31812/123456789/3683.
Texto completoFix, Edward L. Neural Network Based Human Performance Modeling. Fort Belvoir, VA: Defense Technical Information Center, agosto de 1990. http://dx.doi.org/10.21236/ada229822.
Texto completoBhatikar, S. R., R. L. Mahajan, K. Wipke y V. Johnson. Neural Network Based Energy Storage System Modeling for Hybrid Electric Vehicles. Office of Scientific and Technical Information (OSTI), agosto de 1999. http://dx.doi.org/10.2172/935117.
Texto completoAl-Qadi, Imad, Jaime Hernandez, Angeli Jayme, Mojtaba Ziyadi, Erman Gungor, Seunggu Kang, John Harvey et al. The Impact of Wide-Base Tires on Pavement—A National Study. Illinois Center for Transportation, octubre de 2021. http://dx.doi.org/10.36501/0197-9191/21-035.
Texto completoSemerikov, Serhiy, Hanna Kucherova y Dmytro Ocheretin. Neural Network Analytics and Forecasting the Country's Business Climate in Conditions of the Coronavirus Disease (Covid-19). Stylos, diciembre de 2020. http://dx.doi.org/10.31812/123456789/4133.
Texto completoSemerikov, Serhiy, Hanna Kucherova, Vita Los y Dmytro Ocheretin. Neural Network Analytics and Forecasting the Country's Business Climate in Conditions of the Coronavirus Disease (COVID-19). CEUR Workshop Proceedings, abril de 2021. http://dx.doi.org/10.31812//123456789/4364.
Texto completoSemerikov, Serhiy, Illia Teplytskyi, Yuliia Yechkalo, Oksana Markova, Vladimir Soloviev y Arnold Kiv. Computer Simulation of Neural Networks Using Spreadsheets: Dr. Anderson, Welcome Back. [б. в.], junio de 2019. http://dx.doi.org/10.31812/123456789/3178.
Texto completoEngel, Bernard, Yael Edan, James Simon, Hanoch Pasternak y Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, julio de 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
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