Search Results - Error back propagation algorithm through time

Prediction of ground subsidence due to underground mining through time using multilayer feed-forward artificial neural networks and back-propagation algorithm – case study at Mong Duong underground coal mine (Vietnam) ; Прогнозирование просадки грунта в результате подземной добычи с использованием многослойных искусственных нейронных сетей с прямой связью и алгоритма обратного распространения – исследование на примере подземного угольного рудника Монг Дуонг (Вьетнам)

Authors: Q. L. Nguyen Q. M. Nguyen D. T. Tran et al.

Source: Mining Science and Technology (Russia); Vol 6, No 4 (2021); 241–251 ; Горные науки и технологии; Vol 6, No 4 (2021); 241–251 ; 2500-0632

Subject Terms: обратное распространение, subsidence trough, subsidence prediction, artificial neural network, back propagation, мульда просадки, прогнозирование просадки, искусственная нейронная сет

File Description: application/pdf

Relation: https://mst.misis.ru/jour/article/view/305/260; Long N. Q., Ahmad A., Cuong C. X., Canh L. Van. Designing observation lines: a case study of the G9 seam in the Mong Duong colliery. Journal of Mining and Earth Sciences. 2019;60(3):18–24. URL: https://www.researchgate.net/publication/333560617_Designing_observation_lines_a_case_study_of_the_G9_seam_in_the_Mong_Duong_colliery; Ambrožič T., Turk G. Prediction of subsidence due to underground mining by artificial neural networks. Computers and Geosciences. 2003;29(5):627–637. https://doi.org/10.1016/S0098-3004(03)00044-X; King M. A., Watson C. S. Long GPS coordinate time series: Multipath and geometry effects. Journal of Geophysical Research: Solid Earth. 2010;115(4):1–23. https://doi.org/10.1029/2009JB006543; Yang W., Xia X. Prediction of mining subsidence under thin bedrocks and thick unconsolidated layers based on field measurement and artificial neural networks. Computers and Geosciences. 2013;52:199–203. https://doi.org/10.1016/j.cageo.2012.10.017; Cong Khai P., Tran D. T., Nguyen V. H. GNSS/CORS-Based Technology for Real-Time Monitoring of Landslides on Waste Dump – A Case Study at the Deo Nai South Dump, Vietnam. Inżynieria Mineralna. 2020;1(2):181–191. https://doi.org/10.29227/IM-2020-02-23; Hejmanowski R., Witkowski W. T. Suitability assessment of artificial neural network to approximate surface subsidence due to rock mass drainage. Journal of Sustainable Mining. 2015;14(2):101–107. https://doi.org/10.1016/j.jsm.2015.08.014; Zhao K., Chen S. N. Study on artificial neural network method for ground subsidence prediction of metal mine. Procedia Earth and Planetary Science. 2011;2(1):177–182. https://doi.org/10.1016/j.proeps.2011.09.029; Kim K. D., Lee S., Oh H. J. Prediction of ground subsidence in Samcheok City, Korea using artificial neural networks and GIS. Environmental Geology. 2009;58(1):61–70. https://doi.org/10.1007/s00254-008-1492-9; Kim Y., Son M., Moon H. K., Lee S. A. A study on the development of an artificial neural network model for the prediction of ground subsidence over abandoned mines in Korea. Geosystem Engineering. 2017;20(3):163–171. https://doi.org/10.1080/12269328.2016.1254573; Lee S., Park I., Choi J. K. Spatial prediction of ground subsidence susceptibility using an artificial neural network. Environmental Management. 2012;49(2):347–358. https://doi.org/10.1007/s00267-011-9766-5; Hu Q., Deng X., Feng R., Li C., Wang X., Jiang T. Model for calculating the parameter of the Knothe time function based on angle of full subsidence. International Journal of Rock Mechanics and Mining Sciences. 2015;78:19–26. https://doi.org/10.1016/j.ijrmms.2015.04.022; Wang C., Ji H. Analysis on the improved time function model of surface subsidence. Electronic Journal of Geotechnical Engineering. 2015;19:615–627.; Zhanqiang C., Jinzhuang W. Study on time function of surface subsidence the improved Knothe time function. Chinese Journal of Rock Mechanics and Engineering. 2003;9.; Long N. Q., My V. C., Luyen B. K. Divergency verification of predicted values and monitored deformation indicators in specific condition of Thong Nhat underground coal mine (Vietnam). Geoinformatica Polonica. 2016;15:15–22. https://doi.org/10.4467/21995923GP.16.002.5479; Cui X., Miao X., Wang J., Yang S. et al. Improved prediction of differential subsidence caused by underground mining. International Journal of Rock Mechanics and Mining Sciences. 2000;37(4):615–627. https://doi.org/10.1016/S1365-1609(99)00125-2; Gonzalez-Nicieza C., Alvarez-Fernandez M. I., Menendez-Diaz A., Alvarez-Vigil A. E. The influence of time on subsidence in the Central Asturian Coalfield. Bulletin of Engineering Geology and the Environment. 2007;66(3):319–329. https://doi.org/10.1007/s10064-007-0085-2; Liu X., Wang J., Guo J., Yuan H., Li P. Time function of surface subsidence based on Harris model in mined-out area. International Journal of Mining Science and Technology. 2013;23(2):245–248. https://doi.org/10.1016/j.ijmst.2013.04.012; Haykin S. Neural Networks: A Comprehensive Foundation. 2nd Edition. 2004.; Rumelhart D. E., Hinton G. E., Williams R. J. Learning representations by back-propagating errors. Nature. 1986;323:533–536. https://doi.org/10.1038/323533a0; Long N. Q. A novel approach of determining the parameters of Asadi profiling function for predictiong ground subsidence due to inclied coal seam mining at Quang Ninh coal basin. Journal of Mining and Earth Sciences. 2020;61(2):86–95. https://doi.org/10.46326/JMES.2020.61(2).10; Atiya A. F. Learning algorithms for neural networks. California Institute of Technology, Pasadena, California. 1991. URL: https://thesis.library.caltech.edu/3725/1/Atiya_a_1991.pdf; Heaton J. Introduction to neural networks with Java. 2nd Edition. Heaton Research, Inc. 2008. 439 p.; Dayong Y., Jun S. Prediction method for evolutionary neural net of strata subsidence of rock Mass. Mining Safety & Environmental Protection. 2002;29(3):11–13. (In Chin.).; https://mst.misis.ru/jour/article/view/305

Availability: https://mst.misis.ru/jour/article/view/305
https://doi.org/10.17073/2500-0632-2021-4-241-251

Seismic Velocity Inversion via Physical Embedding Recurrent Neural Networks (RNN).

Authors: Lu, Cai Zhang, Chunlong

Source: Applied Sciences (2076-3417); Dec2023, Vol. 13 Issue 24, p13312, 16p

Subject Terms: RECURRENT neural networks, SEISMIC wave velocity, ACOUSTIC wave propagation, FINITE difference method, SEISMIC waves, FINITE differences, SURFACE waves (Seismic waves)

Enterprise Supply Chain Management and Information Assurance Mechanism Based on Error Backpropagation Algorithm.

Authors: Zhang, Qiaoning Liu, Han

Source: Journal of Combinatorial Mathematics & Combinatorial Computing; Dec2025, Vol. 127a, p6655-6674, 20p

Subject Terms: SUPPLY chain management, SUPPLY chains, BACK propagation, ARTIFICIAL intelligence, MACHINE learning

Integrating Error Back-Propagation and Independent Component Analysis Algorithms to Enhance Neural Network Performance.

Authors: Sang-Hoon Oh

Source: International Journal of Contents; Mar2025, Vol. 21 Issue 1, p147-154, 8p

Subject Terms: ARTIFICIAL neural networks, MACHINE learning, INDEPENDENT component analysis, MULTILAYER perceptrons, AUDITORY learning, EAR

Network Security Based on Improved Genetic Algorithm and Weighted Error Back-Propagation Algorithm.

Authors: Junjuan Liang

Source: International Journal of Advanced Computer Science & Applications; 2024, Vol. 15 Issue 11, p211-221, 11p

INTELLIGENT VEHICLE INSPECTION TOOL DESIGN BASED ON FREEMAN CHAIN CODE FOR AUTOMATIC ANNOTATION OF 3D MODELS.

Authors: SHAN CHENG GUOQIANG TAN

Source: Scalable Computing: Practice & Experience; Dec2023, Vol. 24 Issue 4, p769-782, 14p

Subject Terms: BACK propagation, AUTOMOTIVE engineering, ANNOTATIONS, CLASSIFICATION, AUTONOMOUS vehicles

Crime Spatiotemporal Prediction With Fused Objective Function in Time Delay Neural Network

Authors: Anahita Ghazvini Siti Norul Huda Sheikh Abdullah Mohammad Kamrul Hasan et al.

Source: IEEE Access, Vol 8, Pp 115167-115183 (2020)

Subject Terms: Back propagation algorithm, enhanced NARX, NARX, neural network, temporal pattern recognition, quantum geographic information systems, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

File Description: electronic resource

Relation: https://ieeexplore.ieee.org/document/9120031/; https://doaj.org/toc/2169-3536

Access URL: https://doaj.org/article/a7337762d19b442aa003a98ba5643696

Two States Mapping Based Time Series Neural Network Model for Compensation Prediction Residual Error.

Authors: Insung Jung Lockjo Koo Gi-Nam Wang

Source: AIP Conference Proceedings; 11/6/2008, Vol. 1060 Issue 1, p174-177, 4p, 3 Diagrams, 1 Graph

Subject Terms: ARTIFICIAL neural networks, ARTIFICIAL intelligence, SIGNAL processing, BACK propagation, TIME series analysis

Intuitionistic Fuzzy Petri Nets Model Based on Back Propagation Algorithm for Information Services.

Authors: Junhua Xi Kouquan Zheng Jianfeng Ma et al.

Source: Computers, Materials & Continua; 2020, Vol. 63 Issue 2, p605-619, 15p

Subject Terms: BACK propagation, PETRI nets, INFORMATION services, ALGORITHMS, CONTINUOUS functions, FUZZY systems

Theories of Error Back-Propagation in the Brain.

Authors: Whittington, James C.R. Bogacz, Rafal

Source: Trends in Cognitive Sciences. Mar2019, Vol. 23 Issue 3, p235-250. 16p.

Subject Terms: *ERROR analysis in mathematics, *ARTIFICIAL neural networks, *DEEP learning

AVLR-EBP: A Variable Step Size Approach to Speed-up the Convergence of Error Back-Propagation Algorithm.

Authors: Didandeh, Arman Mirbakhsh, Nima Amiri, Ali et al.

Source: Neural Processing Letters; Apr2011, Vol. 33 Issue 2, p201-214, 14p

Subject Terms: NEURAL computers, ARTIFICIAL neural networks, ONLINE data processing, DATA mining, DATABASE searching, CONTENT mining, COMPUTER programming

Analysis and Simulation of the Early Warning Model for Human Resource Management Risk Based on the BP Neural Network.

Authors: Yan, Xue Deng, Xiangwu Sun, Shouheng

Source: Complexity; 11/18/2020, p1-11, 11p

Subject Terms: PERSONNEL management, INFORMATION storage & retrieval systems, BACK propagation, HUMAN resources departments, RISK assessment

Echo Canceller Using Error Back Propagation Algorithm.

Authors: Roy, Soumava Kumar Rodrigues, Crefeda Faviola

Source: 2014 International Conference on Soft Computing & Machine Intelligence; 2014, p98-101, 4p

Graduate enrollment prediction by an Error Back Propagation algorithm based on the Multi-Experiential Particle Swarm Optimization.

Authors: Jia Xu Yan Yang Rui Zhang

Source: 2015 11th International Conference on Natural Computation (ICNC); 2015, p1159-1164, 6p

Diagnostic Classification of Undifferentiated Fevers Using Artificial Neural Network.

Authors: Vasudeva, Shrivathsa Thokur Rao, Shrikantha Sasihithlu Panambur, Navin Karanth et al.

Source: AIP Conference Proceedings; 2020, Vol. 2236 Issue 1, p070001-1-070001-7, 7p

Subject Terms: FEVER, BODY temperature, ARTIFICIAL neural networks, FORECASTING, TARDINESS, ARTIFICIAL intelligence, PREDICTION models, CLASSIFICATION

CNN model optimization and intelligent balance model for material demand forecast.

Authors: Tang, Zheng Ge, Yuemeng

Source: International Journal of Systems Assurance Engineering & Management; Dec2022 Suppl 3, Vol. 13, p978-986, 9p

Smart IoT-based visual target enabled track and field training using image recognition.

Authors: Li, Jia Tian, Shuya Charoenwattana, Sukanya

Source: Soft Computing - A Fusion of Foundations, Methodologies & Applications; Sep2023, Vol. 27 Issue 17, p12571-12585, 15p

Subject Terms: TRACK & field, OBJECT tracking (Computer vision), IMAGE recognition (Computer vision), TRACK & field athletes, SMART cities, CONVOLUTIONAL neural networks, EDGE computing

Evolutionary design of constructive multilayer feedforward neural network.

Authors: Chen, Ching-Han Yao, Tun-Kai Kuo, Chia-Ming et al.

Source: Journal of Vibration & Control; Dec2013, Vol. 19 Issue 16, p2413-2420, 8p

Subject Terms: FEEDFORWARD neural networks, NEURONS, BACK propagation, GENETIC algorithms, NEURAL circuitry

PROPUESTA DE UN SISTEMA NEURO-DBR Y SU APLICACIÓN EN LA PREDICCIÓN DE LA SERIE DE TIEMPO DE LORENZ.

Alternate Title: PROPOSAL OF A NEURO-DBR SYSTEM AND ITS APPLICATI ON TO THE PREDICTION OF LORENZ TIME SERIES.

Authors: Laguado, Lina Morales Cuchango, Helbert Espitia Méndez, José Soriano

Source: Ciencia e Ingenieria Neogranadina. dic2010, Vol. 20 Issue 2, p31-51. 21p. 3 Diagrams, 3 Charts, 7 Graphs.

Subject Terms: Lorenz equations, Boolean algebra, Fuzzy systems, Nonlinear systems, Algorithms

Fast neural network algorithm for solving classification tasks: Batch error back-propagation algorithm.

Authors: Albarakati, Noor Kecman, Vojislav

Source: 2013 Proceedings of IEEE Southeastcon; 2013, p1-8, 8p