Bibliografías temáticas / ONLINE SEQUENTIAL FUZZY EXTREME LEARNING MACHINE / Artículos de revistas

Artículos de revistas sobre el tema "ONLINE SEQUENTIAL FUZZY EXTREME LEARNING MACHINE"

Siga este enlace para ver otros tipos de publicaciones sobre el tema: ONLINE SEQUENTIAL FUZZY EXTREME LEARNING MACHINE.
Autor: Grafiati
Publicado: 11 de septiembre de 2023

Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros

Elija tipo de fuente:

Consulte los 50 mejores artículos de revistas para su investigación sobre el tema "ONLINE SEQUENTIAL FUZZY EXTREME LEARNING MACHINE".

Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.

También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.

Explore artículos de revistas sobre una amplia variedad de disciplinas y organice su bibliografía correctamente.

1

Hai-Jun Rong, Guang-Bin Huang, N. Sundararajan y P. Saratchandran. "Online Sequential Fuzzy Extreme Learning Machine for Function Approximation and Classification Problems". IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 39, n.º 4 (agosto de 2009): 1067–72. http://dx.doi.org/10.1109/tsmcb.2008.2010506.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
2

Wang, Hai, Gang Qian y Xiang-Qian Feng. "Predicting consumer sentiments using online sequential extreme learning machine and intuitionistic fuzzy sets". Neural Computing and Applications 22, n.º 3-4 (5 de febrero de 2012): 479–89. http://dx.doi.org/10.1007/s00521-012-0853-1.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
3

RONG, HAI-JUN, GUANG-BIN HUANG y YONG-QI LIANG. "FUZZY EXTREME LEARNING MACHINE FOR A CLASS OF FUZZY INFERENCE SYSTEMS". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 21, supp02 (31 de octubre de 2013): 51–61. http://dx.doi.org/10.1142/s0218488513400151.

Texto completo
Resumen
Recently an Online Sequential Fuzzy Extreme Learning (OS-Fuzzy-ELM) algorithm has been developed by Rong et al. for the RBF-like fuzzy neural systems where a fuzzy inference system is equivalent to a RBF network under some conditions. In the paper the learning ability of the batch version of OS-Fuzzy-ELM, called as Fuzzy-ELM is further evaluated to train a class of fuzzy inference systems which can not be represented by the RBF networks. The equivalence between the output of the fuzzy system and that of a generalized Single-Hidden Layer Feedforward Network as presented in Huang et al. is shown first, which is then used to prove the validity of the Fuzzy-ELM algorithm. In Fuzzy-ELM, the parameters of the fuzzy membership functions are randomly assigned and then the corresponding consequent parameters are determined analytically. Besides an input variable selection method based on the correlation measure is proposed to select the relevant inputs as the inputs of the fuzzy system. This can avoid the exponential increase of number of fuzzy rules with the increase of dimension of input variables while maintaining the testing performance and reducing the computation burden. Performance comparison of Fuzzy-ELM with other existing algorithms is presented using some real-world regression benchmark problems. The results show that the proposed Fuzzy-ELM produces similar or better accuracies with a significantly lower training time.
Los estilos APA, Harvard, Vancouver, ISO, etc.
4

Yin, Jianchuan y Nini Wang. "An online sequential extreme learning machine for tidal prediction based on improved Gath–Geva fuzzy segmentation". Neurocomputing 174 (enero de 2016): 85–98. http://dx.doi.org/10.1016/j.neucom.2015.02.094.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
5

Zhu, Shuai, Hui Wang, Hui Lv y Huisheng Zhang. "Augmented Online Sequential Quaternion Extreme Learning Machine". Neural Processing Letters 53, n.º 2 (5 de febrero de 2021): 1161–86. http://dx.doi.org/10.1007/s11063-021-10435-8.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
6

Deng, Wan-Yu, Yew-Soon Ong, Puay Siew Tan y Qing-Hua Zheng. "Online sequential reduced kernel extreme learning machine". Neurocomputing 174 (enero de 2016): 72–84. http://dx.doi.org/10.1016/j.neucom.2015.06.087.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
7

Lan, Yuan, Yeng Chai Soh y Guang-Bin Huang. "Ensemble of online sequential extreme learning machine". Neurocomputing 72, n.º 13-15 (agosto de 2009): 3391–95. http://dx.doi.org/10.1016/j.neucom.2009.02.013.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
8

Gu, Yang, Junfa Liu, Yiqiang Chen, Xinlong Jiang y Hanchao Yu. "TOSELM: Timeliness Online Sequential Extreme Learning Machine". Neurocomputing 128 (marzo de 2014): 119–27. http://dx.doi.org/10.1016/j.neucom.2013.02.047.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
9

Scardapane, Simone, Danilo Comminiello, Michele Scarpiniti y Aurelio Uncini. "Online Sequential Extreme Learning Machine With Kernels". IEEE Transactions on Neural Networks and Learning Systems 26, n.º 9 (septiembre de 2015): 2214–20. http://dx.doi.org/10.1109/tnnls.2014.2382094.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
10

Dai, Bo, Chongshi Gu, Erfeng Zhao, Kai Zhu, Wenhan Cao y Xiangnan Qin. "Improved online sequential extreme learning machine for identifying crack behavior in concrete dam". Advances in Structural Engineering 22, n.º 2 (25 de julio de 2018): 402–12. http://dx.doi.org/10.1177/1369433218788635.

Texto completo
Resumen
Prediction models are essential in dam crack behavior identification. Prototype monitoring data arrive sequentially in dam safety monitoring. Given such characteristic, sequential learning algorithms are preferred over batch learning algorithms as they do not require retraining whenever new data are received. A new methodology using the genetic optimized online sequential extreme learning machine and bootstrap confidence intervals is proposed as a practical tool for identifying concrete dam crack behavior. First, online sequential extreme learning machine is adopted to build an online prediction model of crack behavior. The characteristic vector of crack behavior, which is taken as the online sequential extreme learning machine input, is extracted by the statistical model. A genetic algorithm is introduced to optimize the input weights and biases of online sequential extreme learning machine. Second, the BC a method is proposed to produce confidence intervals based on the improved online sequential extreme learning machine prediction. The improved online sequential extreme learning machine for identifying crack behavior is then built. Third, the crack behavior of an actual concrete dam is taken as an example. The capability of the built model for predicting dam crack opening is evaluated. The comparative results demonstrate that the improved online sequential extreme learning machine can provide highly accurate forecasts and reasonably identify crack behavior.
Los estilos APA, Harvard, Vancouver, ISO, etc.
11

Huynh, Hieu Trung, Yonggwan Won y Jinsul Kim. "Hematocrit estimation using online sequential extreme learning machine". Bio-Medical Materials and Engineering 26, s1 (17 de agosto de 2015): S2025—S2032. http://dx.doi.org/10.3233/bme-151507.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
12

Ye, Yibin, Stefano Squartini y Francesco Piazza. "Online sequential extreme learning machine in nonstationary environments". Neurocomputing 116 (septiembre de 2013): 94–101. http://dx.doi.org/10.1016/j.neucom.2011.12.064.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
13

Zhao, Jianwei, Zhihui Wang y Dong Sun Park. "Online sequential extreme learning machine with forgetting mechanism". Neurocomputing 87 (junio de 2012): 79–89. http://dx.doi.org/10.1016/j.neucom.2012.02.003.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
14

Shao, Zhifei y Meng Joo Er. "An online sequential learning algorithm for regularized Extreme Learning Machine". Neurocomputing 173 (enero de 2016): 778–88. http://dx.doi.org/10.1016/j.neucom.2015.08.029.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
15

Guo, Lu, Jing-hua Hao y Min Liu. "An incremental extreme learning machine for online sequential learning problems". Neurocomputing 128 (marzo de 2014): 50–58. http://dx.doi.org/10.1016/j.neucom.2013.03.055.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
16

Mirza, Bilal, Zhiping Lin y Kar-Ann Toh. "Weighted Online Sequential Extreme Learning Machine for Class Imbalance Learning". Neural Processing Letters 38, n.º 3 (27 de febrero de 2013): 465–86. http://dx.doi.org/10.1007/s11063-013-9286-9.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
17

Yu, Hualong, Houjuan Xie, Xibei Yang, Haitao Zou y Shang Gao. "Online sequential extreme learning machine with the increased classes". Computers & Electrical Engineering 90 (marzo de 2021): 107008. http://dx.doi.org/10.1016/j.compeleceng.2021.107008.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
18

Cao, Weipeng, Zhong Ming, Zhiwu Xu, Jiyong Zhang y Qiang Wang. "Online Sequential Extreme Learning Machine With Dynamic Forgetting Factor". IEEE Access 7 (2019): 179746–57. http://dx.doi.org/10.1109/access.2019.2959032.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
19

Zou, Quan-Yi, Xiao-Jun Wang, Chang-Jun Zhou y Qiang Zhang. "The memory degradation based online sequential extreme learning machine". Neurocomputing 275 (enero de 2018): 2864–79. http://dx.doi.org/10.1016/j.neucom.2017.11.030.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
20

Jia, Xibin, Runyuan Wang, Junfa Liu y David M. W. Powers. "A semi-supervised online sequential extreme learning machine method". Neurocomputing 174 (enero de 2016): 168–78. http://dx.doi.org/10.1016/j.neucom.2015.04.102.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
21

Wang, Botao, Shan Huang, Junhao Qiu, Yu Liu y Guoren Wang. "Parallel online sequential extreme learning machine based on MapReduce". Neurocomputing 149 (febrero de 2015): 224–32. http://dx.doi.org/10.1016/j.neucom.2014.03.076.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
22

Xia, Min, Jie Wang, Jia Liu, Liguo Weng y Yiqing Xu. "Density-based semi-supervised online sequential extreme learning machine". Neural Computing and Applications 32, n.º 12 (4 de febrero de 2019): 7747–58. http://dx.doi.org/10.1007/s00521-019-04066-3.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
23

Wang, Lin Lin, Fei Pei y Yong Li Zhu. "Transformer Fault Diagnosis Based on Online Sequential Extreme Learning Machine". Applied Mechanics and Materials 721 (diciembre de 2014): 360–65. http://dx.doi.org/10.4028/www.scientific.net/amm.721.360.

Texto completo
Resumen
Experiment analyzed the main factors that affect the performance of Online Sequential Extreme Learning Machine (OS-ELM). And the experimental comparison show that the OS-ELM in classification performance is better than the support vector machine (SVM) and extreme learning machine (ELM). But there still is not a stable network output now. For this aspect, this article presents the optimization algorithm of integrated Ensemble of online sequential extreme learning machine (EOS-ELM). The algorithm using a limited number of sample data has been applied to transformer fault diagnosis. The time of training and testing can be shortened and the classification accuracy can be improved. The experimental results show that the OS-ELM has better performance in response to online monitoring and real-time data processing.
Los estilos APA, Harvard, Vancouver, ISO, etc.
24

Shukla, Sanyam y Bhagat Singh Raghuwanshi. "Online sequential class-specific extreme learning machine for binary imbalanced learning". Neural Networks 119 (noviembre de 2019): 235–48. http://dx.doi.org/10.1016/j.neunet.2019.08.018.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
25

Atsawaraungsuk, Sarutte, Wasaya Boonphairote, Kritsanapong Somsuk, Chanwit Suwannapong y Suchart Khummanee. "A progressive learning for structural tolerance online sequential extreme learning machine". TELKOMNIKA (Telecommunication Computing Electronics and Control) 21, n.º 5 (1 de octubre de 2023): 1039. http://dx.doi.org/10.12928/telkomnika.v21i5.24564.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
26

AL-Khaleefa, Ahmed, Mohd Ahmad, Azmi Isa, Mona Esa, Ahmed AL-Saffar y Mustafa Hassan. "Feature Adaptive and Cyclic Dynamic Learning Based on Infinite Term Memory Extreme Learning Machine". Applied Sciences 9, n.º 5 (2 de marzo de 2019): 895. http://dx.doi.org/10.3390/app9050895.

Texto completo
Resumen
Online learning is the capability of a machine-learning model to update knowledge without retraining the system when new, labeled data becomes available. Good online learning performance can be achieved through the ability to handle changing features and preserve existing knowledge for future use. This can occur in different real world applications such as Wi-Fi localization and intrusion detection. In this study, we generated a cyclic dynamic generator (CDG), which we used to convert an existing dataset into a time series dataset with cyclic and changing features. Furthermore, we developed the infinite-term memory online sequential extreme learning machine (ITM-OSELM) on the basis of the feature-adaptive online sequential extreme learning machine (FA-OSELM) transfer learning, which incorporates an external memory to preserve old knowledge. This model was compared to the FA-OSELM and online sequential extreme learning machine (OSELM) on the basis of data generated from the CDG using three datasets: UJIndoorLoc, TampereU, and KDD 99. Results corroborate that the ITM-OSELM is superior to the FA-OSELM and OSELM using a statistical t-test. In addition, the accuracy of ITM-OSELM was 91.69% while the accuracy of FA-OSELM and OSELM was 24.39% and 19.56%, respectively.
Los estilos APA, Harvard, Vancouver, ISO, etc.
27

Yan, X. H. y Z. W. Zhou. "A Car-Following Model Using Online Sequential Extreme Learning Machine". Journal of Physics: Conference Series 1848, n.º 1 (1 de abril de 2021): 012095. http://dx.doi.org/10.1088/1742-6596/1848/1/012095.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
28

Xin Ma, Shengkai Zhou y Yibin Li. "Incremental Human Action Recognition with Online Sequential Extreme Learning Machine". International Journal of Advancements in Computing Technology 5, n.º 9 (31 de mayo de 2013): 155–63. http://dx.doi.org/10.4156/ijact.vol5.issue9.19.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
29

Wang, Xingbiao, Bin Gu, Quanyi Zou y Rui Lei. "Local Gravitation Clustering-Based Semisupervised Online Sequential Extreme Learning Machine". Security and Communication Networks 2022 (11 de mayo de 2022): 1–15. http://dx.doi.org/10.1155/2022/1735573.

Texto completo
Resumen
Due to the limited number of labeled samples, semisupervised learning often leads to a considerable empirical distribution mismatch between labeled samples and unlabeled samples. To this end, this paper proposes a novel semisupervised algorithm named Local Gravitation-based Semisupervised Online Sequential Extreme Learning Machine (LGS-OSELM), learning to unlabeled samples follows from easy to difficult. Each sample is formulated as an object with mass and associated with local gravitation generated from its neighbors. The similarity between samples is measurable by the local gravitation measures (centrality CE and coordination CO). First, the LGS-OSELM uses the labeled samples to learn the initialization model by implementing ELM. Second, the unlabeled samples with a high confidence level that is easy to learn are labeled with the pseudo label. Then, these samples are utilized to iterate the neural network by implementing OS-ELM. The proposed approach ultimately realizes effective learning of all samples through successive learning unlabeled samples and iterating neural networks. We implement experiments on several standard benchmark data sets to verify the performance of the proposed LGS-OSELM, which demonstrates that our proposed approach outperforms state-of-the-art methods in terms of accuracy.
Los estilos APA, Harvard, Vancouver, ISO, etc.
30

Zhao, Zhongtang, Xuezhuan Zhao y Lingling Li. "Self labeling online sequential extreme learning machine and it’s application". Journal of Intelligent & Fuzzy Systems 37, n.º 4 (25 de octubre de 2019): 4485–91. http://dx.doi.org/10.3233/jifs-179281.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
31

Singh, Ram Pal, Neelam Dabas, Vikash Chaudhary y Nagendra. "Online Sequential Extreme Learning Machine for watermarking in DWT domain". Neurocomputing 174 (enero de 2016): 238–49. http://dx.doi.org/10.1016/j.neucom.2015.03.115.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
32

Cao, Weipeng, Jinzhu Gao, Zhong Ming, Shubin Cai y Zhiguang Shan. "Fuzziness-based online sequential extreme learning machine for classification problems". Soft Computing 22, n.º 11 (7 de febrero de 2018): 3487–94. http://dx.doi.org/10.1007/s00500-018-3021-4.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
33

Xie, Shan Juan, JuCheng Yang, Hui Gong, Sook Yoon y Dong Sun Park. "Intelligent fingerprint quality analysis using online sequential extreme learning machine". Soft Computing 16, n.º 9 (24 de febrero de 2012): 1555–68. http://dx.doi.org/10.1007/s00500-012-0828-2.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
34

Kale, Archana P., Sumedh Sonawane, Revati M. Wahul y Manisha A. Dudhedia. "Improved Genetic Optimized Feature Selection for Online Sequential Extreme Learning Machine". Ingénierie des systèmes d information 27, n.º 5 (31 de octubre de 2022): 843–48. http://dx.doi.org/10.18280/isi.270519.

Texto completo
Resumen
Extreme learning machine (ELM) is a rapid classifier, evolved for batch learning mode which is not suitable for sequential input. As retrieving of data from new inventory which is leads to time extended process. Therefore, online sequential ELM (OSELM) algorithm is progressed to handle the sequential input in which data is read 1 by 1 or chunk by chunk mode. The overall system generalization performance may devalue because of the amalgamation of random initialization of OS-ELM and the presence of redundant and irrelevant features. To resolve the said problem, this paper proposes a correspondence improved genetic optimized feature selection paradigm for sequential input (IG-OSELM) for radial basis or function by using clinical datasets. For performance comparison, the proposed paradigm experimented and evaluated for ELM, improved genetic optimized for ELM classifier (IG-ELM), OS-ELM, IG-OSELM. Experimental results are calculated and analyzed accordingly. The comparative results analysis illustrates that IG-ELM provides 10.94% improved accuracy with 43.25% features as compared to ELM.
Los estilos APA, Harvard, Vancouver, ISO, etc.
35

Yang, Rui, Yongbao Liu, Xing He y Zhimeng Liu. "Gas Turbine Model Identification Based on Online Sequential Regularization Extreme Learning Machine with a Forgetting Factor". Energies 16, n.º 1 (27 de diciembre de 2022): 304. http://dx.doi.org/10.3390/en16010304.

Texto completo
Resumen
Due to the advantages of high convergence accuracy, fast training speed, and good generalization performance, the extreme learning machine is widely used in model identification. However, a gas turbine is a complex nonlinear system, and its sampling data are often time-sensitive and have measurement noise. This article proposes an online sequential regularization extreme learning machine algorithm based on the forgetting factor (FOS_RELM) to improve gas turbine identification performance. The proposed FOS_RELM not only retains the advantages of the extreme learning machine algorithm but also enhances the learning effect by rapidly discarding obsolete data during the learning process and improves the anti-interference performance by using the regularization principle. A detailed performance comparison of the FOS_RELM with the extreme learning machine algorithm and regularized extreme learning machine algorithm is carried out in the model identification of a gas turbine. The results show that the FOS_RELM has higher accuracy and better robustness than the extreme learning machine algorithm and regularized extreme learning machine algorithm. All in all, the proposed algorithm provides a candidate technique for modeling actual gas turbine units.
Los estilos APA, Harvard, Vancouver, ISO, etc.
36

Zhou, Zhiyu, Jiangfei Ji, Yaming Wang, Zefei Zhu y Ji Chen. "Hybrid regression model via multivariate adaptive regression spline and online sequential extreme learning machine and its application in vision servo system". International Journal of Advanced Robotic Systems 19, n.º 3 (1 de mayo de 2022): 172988062211086. http://dx.doi.org/10.1177/17298806221108603.

Texto completo
Resumen
To solve the problems of slow convergence speed, poor robustness, and complex calculation of image Jacobian matrix in image-based visual servo system, a hybrid regression model based on multiple adaptive regression spline and online sequential extreme learning machine is proposed to predict the product of pseudo inverse of image Jacobian matrix and image feature error and online sequential extreme learning machine is proposed to predict the product of pseudo inverse of image Jacobian matrix and image feature error. In MOS-ELM, MARS is used to evaluate the importance of input features and select specific features as the input features of online sequential extreme learning machine, so as to obtain better generalization performance and increase the stability of regression model. Finally, the method is applied to the speed predictive control of the manipulator end effector controlled by image-based visual servo and the prediction of machine learning data sets. Experimental results show that the algorithm has high prediction accuracy on machine learning data sets and good control performance in image-based visual servo.
Los estilos APA, Harvard, Vancouver, ISO, etc.
37

Peng, Xiuyan, Bo Wang, Lanyong Zhang y Peng Su. "Adaptive Online Sequential Extreme Learning Machine with Kernels for Online Ship Power Prediction". Energies 14, n.º 17 (29 de agosto de 2021): 5371. http://dx.doi.org/10.3390/en14175371.

Texto completo
Resumen
With the in-depth penetration of renewable energy in the shipboard power system, the uncertainty of its output power and the variability of sea conditions have brought severe challenges to the control of shipboard integrated power system. In order to provide additional accurate signals to the power control system to eliminate the influence of uncertain factors, this study proposed an adaptive kernel based online sequential extreme learning machine to accurately predict shipboard electric power fluctuation online. Three adaptive factors are introduced, which control the kernel function scale adaptively to ensure the accuracy and speed of the algorithm. The electric power fluctuation data of real-ship under two different sea conditions are used to verify the effectiveness of the algorithm. The simulation results clearly demonstrate that in the case of ship power fluctuation prediction, the proposed method can not only meet the rapidity demand of real-time control system, but also provide accurate prediction results.
Los estilos APA, Harvard, Vancouver, ISO, etc.
38

Bielskus, Jonas, Violeta Motuzienė, Tatjana Vilutienė y Audrius Indriulionis. "Occupancy Prediction Using Differential Evolution Online Sequential Extreme Learning Machine Model". Energies 13, n.º 15 (4 de agosto de 2020): 4033. http://dx.doi.org/10.3390/en13154033.

Texto completo
Resumen
Despite increasing energy efficiency requirements, the full potential of energy efficiency is still unlocked; many buildings in the EU tend to consume more energy than predicted. Gathering data and developing models to predict occupants’ behaviour is seen as the next frontier in sustainable design. Measurements in the analysed open-space office showed accordingly 3.5 and 2.7 times lower occupancy compared to the ones given by DesignBuilder’s and EN 16798-1. This proves that proposed occupancy patterns are only suitable for typical open-space offices. The results of the previous studies and proposed occupancy prediction models have limited applications and limited accuracies. In this paper, the hybrid differential evolution online sequential extreme learning machine (DE-OSELM) model was applied for building occupants’ presence prediction in open-space office. The model was not previously applied in this area of research. It was found that prediction using experimentally gained indoor and outdoor parameters for the whole analysed period resulted in a correlation coefficient R2 = 0.72. The best correlation was found with indoor CO2 concentration—R2 = 0.71 for the analysed period. It was concluded that a 4 week measurement period was sufficient for the prediction of the building’s occupancy and that DE-OSELM is a fast and reliable model suitable for this purpose.
Los estilos APA, Harvard, Vancouver, ISO, etc.
39

Atsawaraungsuk, Sarutte y Tatpong Katanyukul. "Sin Activation Structural Tolerance of Online Sequential Circular Extreme Learning Machine". International Journal of Technology 8, n.º 4 (31 de julio de 2017): 601. http://dx.doi.org/10.14716/ijtech.v8i4.9476.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
40

AlDahoul, Nouar y Zaw Zaw Htike. "Online Sequential Extreme Learning Machine Based Functional Magnetic Resonance Imaging Decoder". Advanced Science Letters 21, n.º 11 (1 de noviembre de 2015): 3489–93. http://dx.doi.org/10.1166/asl.2015.6595.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
41

Huang, Shan, Botao Wang, Junhao Qiu, Jitao Yao, Guoren Wang y Ge Yu. "Parallel ensemble of online sequential extreme learning machine based on MapReduce". Neurocomputing 174 (enero de 2016): 352–67. http://dx.doi.org/10.1016/j.neucom.2015.04.105.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
42

Lim, JunSeok. "Partitioned online sequential extreme learning machine for large ordered system modeling". Neurocomputing 102 (febrero de 2013): 59–64. http://dx.doi.org/10.1016/j.neucom.2011.12.049.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
43

Wei, Liyun, Lidan Wang, Yunfei Li y Shukai Duan. "Ensemble of online sequential extreme learning machine based on cross-validation". Journal of Physics: Conference Series 1550 (mayo de 2020): 032156. http://dx.doi.org/10.1088/1742-6596/1550/3/032156.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
44

Wang, Xinying y Min Han. "Improved extreme learning machine for multivariate time series online sequential prediction". Engineering Applications of Artificial Intelligence 40 (abril de 2015): 28–36. http://dx.doi.org/10.1016/j.engappai.2014.12.013.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
45

Jiang, Xinlong, Junfa Liu, Yiqiang Chen, Dingjun Liu, Yang Gu y Zhenyu Chen. "Feature Adaptive Online Sequential Extreme Learning Machine for lifelong indoor localization". Neural Computing and Applications 27, n.º 1 (26 de septiembre de 2014): 215–25. http://dx.doi.org/10.1007/s00521-014-1714-x.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
46

Vong, Chi-Man, Weng-Fai Ip, Chi-Chong Chiu y Pak-Kin Wong. "Imbalanced Learning for Air Pollution by Meta-Cognitive Online Sequential Extreme Learning Machine". Cognitive Computation 7, n.º 3 (26 de agosto de 2014): 381–91. http://dx.doi.org/10.1007/s12559-014-9301-0.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
47

Zhou, Xinran, Zijian Liu y Congxu Zhu. "Online Regularized and Kernelized Extreme Learning Machines with Forgetting Mechanism". Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/938548.

Texto completo
Resumen
To apply the single hidden-layer feedforward neural networks (SLFN) to identify time-varying system, online regularized extreme learning machine (ELM) with forgetting mechanism (FORELM) and online kernelized ELM with forgetting mechanism (FOKELM) are presented in this paper. The FORELM updates the output weights of SLFN recursively by using Sherman-Morrison formula, and it combines advantages of online sequential ELM with forgetting mechanism (FOS-ELM) and regularized online sequential ELM (ReOS-ELM); that is, it can capture the latest properties of identified system by studying a certain number of the newest samples and also can avoid issue of ill-conditioned matrix inversion by regularization. The FOKELM tackles the problem of matrix expansion of kernel based incremental ELM (KB-IELM) by deleting the oldest sample according to the block matrix inverse formula when samples occur continually. The experimental results show that the proposed FORELM and FOKELM have better stability than FOS-ELM and have higher accuracy than ReOS-ELM in nonstationary environments; moreover, FORELM and FOKELM have time efficiencies superiority over dynamic regression extreme learning machine (DR-ELM) under certain conditions.
Los estilos APA, Harvard, Vancouver, ISO, etc.
48

Liu, Yang, Bo He, Diya Dong, Yue Shen, Tianhong Yan, Rui Nian y Amaury Lendasse. "Particle Swarm Optimization Based Selective Ensemble of Online Sequential Extreme Learning Machine". Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/504120.

Texto completo
Resumen
A novel particle swarm optimization based selective ensemble (PSOSEN) of online sequential extreme learning machine (OS-ELM) is proposed. It is based on the original OS-ELM with an adaptive selective ensemble framework. Two novel insights are proposed in this paper. First, a novel selective ensemble algorithm referred to as particle swarm optimization selective ensemble is proposed, noting that PSOSEN is a general selective ensemble method which is applicable to any learning algorithms, including batch learning and online learning. Second, an adaptive selective ensemble framework for online learning is designed to balance the accuracy and speed of the algorithm. Experiments for both regression and classification problems with UCI data sets are carried out. Comparisons between OS-ELM, simple ensemble OS-ELM (EOS-ELM), genetic algorithm based selective ensemble (GASEN) of OS-ELM, and the proposed particle swarm optimization based selective ensemble of OS-ELM empirically show that the proposed algorithm achieves good generalization performance and fast learning speed.
Los estilos APA, Harvard, Vancouver, ISO, etc.
49

Zhao, Feiyu, Buyun Sheng, Xiyan Yin, Hui Wang, Xincheng Lu y Yuncheng Zhao. "An Online Rapid Mesh Segmentation Method Based on an Online Sequential Extreme Learning Machine". IEEE Access 7 (2019): 109094–110. http://dx.doi.org/10.1109/access.2019.2933551.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
50

Zhang, Yubin, Zhengying Wei, Lei Zhang, Qinyin Lin y Jun Du. "Improved online sequential extreme learning machine for simulation of daily reference evapotranspiration". Tecnología y ciencias del agua 08, n.º 2 (10 de marzo de 2017): 127–40. http://dx.doi.org/10.24850/j-tyca-2017-02-12.

Texto completo
Los estilos APA, Harvard, Vancouver, ISO, etc.
Ofrecemos descuentos en todos los planes premium para autores cuyas obras están incluidas en selecciones literarias temáticas. ¡Contáctenos para obtener un código promocional único!

Pasar a la bibliografía