Artículos de revistas sobre el tema "EMD - Neural networks"
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Zheng, Jing Wen, Shi Xiao Li y Yang Kun. "A New Hybrid Model for Forecasting Crude Oil Price and the Techniques in the Model". Advanced Materials Research 974 (junio de 2014): 310–17. http://dx.doi.org/10.4028/www.scientific.net/amr.974.310.
Texto completoSaâdaoui, Foued y Othman Ben Messaoud. "Multiscaled Neural Autoregressive Distributed Lag: A New Empirical Mode Decomposition Model for Nonlinear Time Series Forecasting". International Journal of Neural Systems 30, n.º 08 (26 de junio de 2020): 2050039. http://dx.doi.org/10.1142/s0129065720500392.
Texto completoLei, Yu, Danning Zhao y Hongbing Cai. "Ultra Short-term Prediction of Pole Coordinates via Combination of Empirical Mode Decomposition and Neural Networks". Artificial Satellites 51, n.º 4 (1 de diciembre de 2016): 149–61. http://dx.doi.org/10.1515/arsa-2016-0013.
Texto completoGe, Yujia, Yurong Nan y Lijun Bai. "A Hybrid Prediction Model for Solar Radiation Based on Long Short-Term Memory, Empirical Mode Decomposition, and Solar Profiles for Energy Harvesting Wireless Sensor Networks". Energies 12, n.º 24 (13 de diciembre de 2019): 4762. http://dx.doi.org/10.3390/en12244762.
Texto completoJiang, Qi, Yuxin Cheng, Haozhe Le, Chunquan Li y Peter X. Liu. "A Stacking Learning Model Based on Multiple Similar Days for Short-Term Load Forecasting". Mathematics 10, n.º 14 (13 de julio de 2022): 2446. http://dx.doi.org/10.3390/math10142446.
Texto completoHuang, Xiaoxin y Xiuxiu Chen. "A Quantitative Model of International Trade Based on Deep Neural Network". Computational Intelligence and Neuroscience 2022 (31 de mayo de 2022): 1–11. http://dx.doi.org/10.1155/2022/9811358.
Texto completoZhou, Shuyi, Brandon J. Bethel, Wenjin Sun, Yang Zhao, Wenhong Xie y Changming Dong. "Improving Significant Wave Height Forecasts Using a Joint Empirical Mode Decomposition–Long Short-Term Memory Network". Journal of Marine Science and Engineering 9, n.º 7 (5 de julio de 2021): 744. http://dx.doi.org/10.3390/jmse9070744.
Texto completoZhang, Boning. "Foreign exchange rates forecasting with an EMD-LSTM neural networks model". Journal of Physics: Conference Series 1053 (julio de 2018): 012005. http://dx.doi.org/10.1088/1742-6596/1053/1/012005.
Texto completoChengzhao, Zhang, Pan Heiping y Zhou Ke. "Comparison of Back Propagation Neural Networks and EMD-Based Neural Networks in Forecasting the Three Major Asian Stock Markets". Journal of Applied Sciences 15, n.º 1 (15 de diciembre de 2014): 90–99. http://dx.doi.org/10.3923/jas.2015.90.99.
Texto completoShu, Wangwei y Qiang Gao. "Forecasting Stock Price Based on Frequency Components by EMD and Neural Networks". IEEE Access 8 (2020): 206388–95. http://dx.doi.org/10.1109/access.2020.3037681.
Texto completoTeng, Xian Bin, Jun Dong Zhang, Shi Hai Zhang y Ran Ran Wang. "Fault Diagnosis of Diesel Engine Based on Wavelet Analysis, EMD and Neural Networks". Advanced Materials Research 211-212 (febrero de 2011): 1031–35. http://dx.doi.org/10.4028/www.scientific.net/amr.211-212.1031.
Texto completoMEHBOOB, ZAREEN y HUJUN YIN. "INFORMATION QUANTIFICATION OF EMPIRICAL MODE DECOMPOSITION AND APPLICATIONS TO FIELD POTENTIALS". International Journal of Neural Systems 21, n.º 01 (febrero de 2011): 49–63. http://dx.doi.org/10.1142/s012906571100264x.
Texto completoLin, Hualing y Qiubi Sun. "Crude Oil Prices Forecasting: An Approach of Using CEEMDAN-Based Multi-Layer Gated Recurrent Unit Networks". Energies 13, n.º 7 (25 de marzo de 2020): 1543. http://dx.doi.org/10.3390/en13071543.
Texto completoGui, Sibo, Meng Shi, Zhaolong Li, Haitao Wu, Quansheng Ren y Jianye Zhao. "A Deep-Learning-Based Method for Optical Transmission Link Assessment Applied to Optical Clock Comparisons". Photonics 10, n.º 8 (10 de agosto de 2023): 920. http://dx.doi.org/10.3390/photonics10080920.
Texto completoHassard, Alan. "Investigaton of Eye Movement Desensitization in Pain Clinic Patients". Behavioural and Cognitive Psychotherapy 23, n.º 2 (abril de 1995): 177–85. http://dx.doi.org/10.1017/s1352465800014429.
Texto completoHU, Niaoqing. "Fault Diagnosis for Planetary Gearbox Based on EMD and Deep Convolutional Neural Networks". Journal of Mechanical Engineering 55, n.º 7 (2019): 9. http://dx.doi.org/10.3901/jme.2019.07.009.
Texto completoCarmona, A. M. y G. Poveda. "Prediction of mean monthly river discharges in Colombia through Empirical Mode Decomposition". Proceedings of the International Association of Hydrological Sciences 366 (10 de abril de 2015): 172. http://dx.doi.org/10.5194/piahs-366-172-2015.
Texto completoLi, Chao, Quanjie Guo, Lei Shao, Ji Li y Han Wu. "Research on Short-Term Load Forecasting Based on Optimized GRU Neural Network". Electronics 11, n.º 22 (21 de noviembre de 2022): 3834. http://dx.doi.org/10.3390/electronics11223834.
Texto completoCenteno-Bautista, Manuel A., Angel H. Rangel-Rodriguez, Andrea V. Perez-Sanchez, Juan P. Amezquita-Sanchez, David Granados-Lieberman y Martin Valtierra-Rodriguez. "Electrocardiogram Analysis by Means of Empirical Mode Decomposition-Based Methods and Convolutional Neural Networks for Sudden Cardiac Death Detection". Applied Sciences 13, n.º 6 (10 de marzo de 2023): 3569. http://dx.doi.org/10.3390/app13063569.
Texto completoWu, Jian Hua, Zheng Qiang Yao, Y. Jin, H. B. Xie, Y. S. Zhao y L. Ch Xu. "Application of Hilbert-Huang Transform to Predict Grinding Surface Quality On-Line". Key Engineering Materials 304-305 (febrero de 2006): 227–31. http://dx.doi.org/10.4028/www.scientific.net/kem.304-305.227.
Texto completoMbatha, Nkanyiso y Hassan Bencherif. "Time Series Analysis and Forecasting Using a Novel Hybrid LSTM Data-Driven Model Based on Empirical Wavelet Transform Applied to Total Column of Ozone at Buenos Aires, Argentina (1966–2017)". Atmosphere 11, n.º 5 (30 de abril de 2020): 457. http://dx.doi.org/10.3390/atmos11050457.
Texto completoWang, Yijun, Peiqian Guo, Nan Ma y Guowei Liu. "Robust Wavelet Transform Neural-Network-Based Short-Term Load Forecasting for Power Distribution Networks". Sustainability 15, n.º 1 (24 de diciembre de 2022): 296. http://dx.doi.org/10.3390/su15010296.
Texto completoFeng, Zhijie, Po Hu, Shuiqing Li y Dongxue Mo. "Prediction of Significant Wave Height in Offshore China Based on the Machine Learning Method". Journal of Marine Science and Engineering 10, n.º 6 (20 de junio de 2022): 836. http://dx.doi.org/10.3390/jmse10060836.
Texto completoAhmed, Ammar, Youssef Serrestou, Kosai Raoof y Jean-François Diouris. "Empirical Mode Decomposition-Based Feature Extraction for Environmental Sound Classification". Sensors 22, n.º 20 (11 de octubre de 2022): 7717. http://dx.doi.org/10.3390/s22207717.
Texto completoPopa, Stefan Lucian, Teodora Surdea-Blaga, Dan Lucian Dumitrascu, Giuseppe Chiarioni, Edoardo Savarino, Liliana David, Abdulrahman Ismaiel et al. "Automatic Diagnosis of High-Resolution Esophageal Manometry Using Artificial Intelligence". Journal of Gastrointestinal and Liver Diseases 31, n.º 4 (16 de diciembre de 2022): 383–89. http://dx.doi.org/10.15403/jgld-4525.
Texto completoDryuchenko, M. A. y A. A. Sirota. "Image stegoanalysis using deep neural networks and heteroassociative integral transformations". Prikladnaya Diskretnaya Matematika, n.º 55 (2022): 35–58. http://dx.doi.org/10.17223/20710410/55/3.
Texto completoJiao, Xiaoxuan, Bo Jing, Yifeng Huang, Juan Li y Guangyue Xu. "Research on fault diagnosis of airborne fuel pump based on EMD and probabilistic neural networks". Microelectronics Reliability 75 (agosto de 2017): 296–308. http://dx.doi.org/10.1016/j.microrel.2017.03.007.
Texto completoLiu, Die, Yihao Bao, Yingying He y Likai Zhang. "A Data Loss Recovery Technique Using EMD-BiGRU Algorithm for Structural Health Monitoring". Applied Sciences 11, n.º 21 (27 de octubre de 2021): 10072. http://dx.doi.org/10.3390/app112110072.
Texto completoKang, Aiqing, Qingxiong Tan, Xiaohui Yuan, Xiaohui Lei y Yanbin Yuan. "Short-Term Wind Speed Prediction Using EEMD-LSSVM Model". Advances in Meteorology 2017 (2017): 1–22. http://dx.doi.org/10.1155/2017/6856139.
Texto completoMa, Yu. "Two models for predicting stock prices in combination with LSTM". Highlights in Business, Economics and Management 5 (16 de febrero de 2023): 664–73. http://dx.doi.org/10.54097/hbem.v5i.5256.
Texto completoYu, Jing, Feng Ding, Chenghao Guo y Yabin Wang. "System load trend prediction method based on IF-EMD-LSTM". International Journal of Distributed Sensor Networks 15, n.º 8 (agosto de 2019): 155014771986765. http://dx.doi.org/10.1177/1550147719867655.
Texto completoGuerrero-Sánchez, Alma E., Edgar A. Rivas-Araiza, Mariano Garduño-Aparicio, Saul Tovar-Arriaga, Juvenal Rodriguez-Resendiz y Manuel Toledano-Ayala. "A Novel Methodology for Classifying Electrical Disturbances Using Deep Neural Networks". Technologies 11, n.º 4 (21 de junio de 2023): 82. http://dx.doi.org/10.3390/technologies11040082.
Texto completoCao, Zhiyong, Zhijuan Cao, Hongwei Zhao, Jiajun Xu, Guangyong Zhang, Yi Li, Yufei Su, Ling Lou, Xiujuan Yang y Zhaobing Gu. "Using Empirical Modal Decomposition to Improve the Daily Milk Yield Prediction of Cows". Wireless Communications and Mobile Computing 2022 (11 de julio de 2022): 1–7. http://dx.doi.org/10.1155/2022/1685841.
Texto completoSadrawi, Muammar, Shou-Zen Fan, Maysam F. Abbod, Kuo-Kuang Jen y Jiann-Shing Shieh. "Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks". BioMed Research International 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/536863.
Texto completoJin, Zebin, Yixiao Jin y Zhiyun Chen. "Empirical mode decomposition using deep learning model for financial market forecasting". PeerJ Computer Science 8 (14 de septiembre de 2022): e1076. http://dx.doi.org/10.7717/peerj-cs.1076.
Texto completoRedwan, Sadi M., Md Rashed-Al-Mahfuz y Md Ekramul Hamid. "Recognizing Command Words using Deep Recurrent Neural Network for Both Acoustic and Throat Speech". European Journal of Information Technologies and Computer Science 3, n.º 2 (22 de mayo de 2023): 7–13. http://dx.doi.org/10.24018/compute.2023.3.2.88.
Texto completoCamarena-Martinez, David, Martin Valtierra-Rodriguez, Arturo Garcia-Perez, Roque Alfredo Osornio-Rios y Rene de Jesus Romero-Troncoso. "Empirical Mode Decomposition and Neural Networks on FPGA for Fault Diagnosis in Induction Motors". Scientific World Journal 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/908140.
Texto completoZheng, Huiting, Jiabin Yuan y Long Chen. "Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation". Energies 10, n.º 8 (8 de agosto de 2017): 1168. http://dx.doi.org/10.3390/en10081168.
Texto completoXu, Da-Chuan, Huai-Shu Hou, Cai-Xia Liu y Chao-Fei Jiao. "Defect type identification of thin-walled stainless steel seamless pipe based on eddy current testing". Insight - Non-Destructive Testing and Condition Monitoring 63, n.º 12 (1 de diciembre de 2021): 697–703. http://dx.doi.org/10.1784/insi.2021.63.12.697.
Texto completoRofii, Faqih, Agus Naba, Hari Arief Dharmawan y Fachrudin Hunaini. "Development of empirical mode decomposition based neural network for power quality disturbances classification". EUREKA: Physics and Engineering, n.º 2 (31 de marzo de 2022): 28–44. http://dx.doi.org/10.21303/2461-4262.2022.002046.
Texto completoAltuve, Miguel, Paula Lizarazo y Javier Villamizar. "Human activity recognition using improved complete ensemble EMD with adaptive noise and long short-term memory neural networks". Biocybernetics and Biomedical Engineering 40, n.º 3 (julio de 2020): 901–9. http://dx.doi.org/10.1016/j.bbe.2020.04.007.
Texto completoAsghar, Muhammad Adeel, Muhammad Jamil Khan, Muhammad Rizwan, Raja Majid Mehmood y Sun-Hee Kim. "An Innovative Multi-Model Neural Network Approach for Feature Selection in Emotion Recognition Using Deep Feature Clustering". Sensors 20, n.º 13 (5 de julio de 2020): 3765. http://dx.doi.org/10.3390/s20133765.
Texto completoWang, Dongyu, Xiwen Cui y Dongxiao Niu. "Wind Power Forecasting Based on LSTM Improved by EMD-PCA-RF". Sustainability 14, n.º 12 (15 de junio de 2022): 7307. http://dx.doi.org/10.3390/su14127307.
Texto completoGao, Hongbo, Shuang Qiu, Jun Fang, Nan Ma, Jiye Wang, Kun Cheng, Hui Wang et al. "Short-Term Prediction of PV Power Based on Combined Modal Decomposition and NARX-LSTM-LightGBM". Sustainability 15, n.º 10 (18 de mayo de 2023): 8266. http://dx.doi.org/10.3390/su15108266.
Texto completoDiez, Pablo F., Vicente A. Mut, Eric Laciar, Abel Torres y Enrique M. Avila Perona. "FEATURES EXTRACTION METHOD FOR BRAIN-MACHINE COMMUNICATION BASED ON THE EMPIRICAL MODE DECOMPOSITION". Biomedical Engineering: Applications, Basis and Communications 25, n.º 06 (diciembre de 2013): 1350058. http://dx.doi.org/10.4015/s1016237213500580.
Texto completoJaramillo-Morán, Miguel A., Daniel Fernández-Martínez, Agustín García-García y Diego Carmona-Fernández. "Improving Artificial Intelligence Forecasting Models Performance with Data Preprocessing: European Union Allowance Prices Case Study". Energies 14, n.º 23 (23 de noviembre de 2021): 7845. http://dx.doi.org/10.3390/en14237845.
Texto completoZeng, Wei, Mengqing Li, Chengzhi Yuan, Qinghui Wang, Fenglin Liu y Ying Wang. "Classification of focal and non focal EEG signals using empirical mode decomposition (EMD), phase space reconstruction (PSR) and neural networks". Artificial Intelligence Review 52, n.º 1 (3 de abril de 2019): 625–47. http://dx.doi.org/10.1007/s10462-019-09698-4.
Texto completoMohsenimanesh, Ahmad, Evgueniy Entchev y Filip Bosnjak. "Hybrid Model Based on an SD Selection, CEEMDAN, and Deep Learning for Short-Term Load Forecasting of an Electric Vehicle Fleet". Applied Sciences 12, n.º 18 (16 de septiembre de 2022): 9288. http://dx.doi.org/10.3390/app12189288.
Texto completoDang, Sanlei, Long Peng, Jingming Zhao, Jiajie Li y Zhengmin Kong. "A Quantile Regression Random Forest-Based Short-Term Load Probabilistic Forecasting Method". Energies 15, n.º 2 (17 de enero de 2022): 663. http://dx.doi.org/10.3390/en15020663.
Texto completoZhang, Yixiang, Zenggui Gao, Jiachen Sun y Lilan Liu. "Machine-Learning Algorithms for Process Condition Data-Based Inclusion Prediction in Continuous-Casting Process: A Case Study". Sensors 23, n.º 15 (27 de julio de 2023): 6719. http://dx.doi.org/10.3390/s23156719.
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