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Thematische Bibliographien / ONLINE SEQUENTIAL FUZZY EXTREME LEARNING MACHINE / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „ONLINE SEQUENTIAL FUZZY EXTREME LEARNING MACHINE“

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Veröffentlicht am 11. September 2023

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1

Hai-Jun Rong, Guang-Bin Huang, N. Sundararajan und 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, Nr. 4 (August 2009): 1067–72. http://dx.doi.org/10.1109/tsmcb.2008.2010506.

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2

Wang, Hai, Gang Qian und Xiang-Qian Feng. „Predicting consumer sentiments using online sequential extreme learning machine and intuitionistic fuzzy sets“. Neural Computing and Applications 22, Nr. 3-4 (05.02.2012): 479–89. http://dx.doi.org/10.1007/s00521-012-0853-1.

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3

RONG, HAI-JUN, GUANG-BIN HUANG und 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 (31.10.2013): 51–61. http://dx.doi.org/10.1142/s0218488513400151.

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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.

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4

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

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5

Zhu, Shuai, Hui Wang, Hui Lv und Huisheng Zhang. „Augmented Online Sequential Quaternion Extreme Learning Machine“. Neural Processing Letters 53, Nr. 2 (05.02.2021): 1161–86. http://dx.doi.org/10.1007/s11063-021-10435-8.

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6

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

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7

Lan, Yuan, Yeng Chai Soh und Guang-Bin Huang. „Ensemble of online sequential extreme learning machine“. Neurocomputing 72, Nr. 13-15 (August 2009): 3391–95. http://dx.doi.org/10.1016/j.neucom.2009.02.013.

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8

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

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9

Scardapane, Simone, Danilo Comminiello, Michele Scarpiniti und Aurelio Uncini. „Online Sequential Extreme Learning Machine With Kernels“. IEEE Transactions on Neural Networks and Learning Systems 26, Nr. 9 (September 2015): 2214–20. http://dx.doi.org/10.1109/tnnls.2014.2382094.

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10

Dai, Bo, Chongshi Gu, Erfeng Zhao, Kai Zhu, Wenhan Cao und Xiangnan Qin. „Improved online sequential extreme learning machine for identifying crack behavior in concrete dam“. Advances in Structural Engineering 22, Nr. 2 (25.07.2018): 402–12. http://dx.doi.org/10.1177/1369433218788635.

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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.

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11

Huynh, Hieu Trung, Yonggwan Won und Jinsul Kim. „Hematocrit estimation using online sequential extreme learning machine“. Bio-Medical Materials and Engineering 26, s1 (17.08.2015): S2025—S2032. http://dx.doi.org/10.3233/bme-151507.

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12

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

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13

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

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14

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

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15

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

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16

Mirza, Bilal, Zhiping Lin und Kar-Ann Toh. „Weighted Online Sequential Extreme Learning Machine for Class Imbalance Learning“. Neural Processing Letters 38, Nr. 3 (27.02.2013): 465–86. http://dx.doi.org/10.1007/s11063-013-9286-9.

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17

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

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18

Cao, Weipeng, Zhong Ming, Zhiwu Xu, Jiyong Zhang und 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.

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19

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

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20

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

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21

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

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22

Xia, Min, Jie Wang, Jia Liu, Liguo Weng und Yiqing Xu. „Density-based semi-supervised online sequential extreme learning machine“. Neural Computing and Applications 32, Nr. 12 (04.02.2019): 7747–58. http://dx.doi.org/10.1007/s00521-019-04066-3.

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23

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

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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.

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24

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

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25

Atsawaraungsuk, Sarutte, Wasaya Boonphairote, Kritsanapong Somsuk, Chanwit Suwannapong und Suchart Khummanee. „A progressive learning for structural tolerance online sequential extreme learning machine“. TELKOMNIKA (Telecommunication Computing Electronics and Control) 21, Nr. 5 (01.10.2023): 1039. http://dx.doi.org/10.12928/telkomnika.v21i5.24564.

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26

AL-Khaleefa, Ahmed, Mohd Ahmad, Azmi Isa, Mona Esa, Ahmed AL-Saffar und Mustafa Hassan. „Feature Adaptive and Cyclic Dynamic Learning Based on Infinite Term Memory Extreme Learning Machine“. Applied Sciences 9, Nr. 5 (02.03.2019): 895. http://dx.doi.org/10.3390/app9050895.

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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.

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27

Yan, X. H., und Z. W. Zhou. „A Car-Following Model Using Online Sequential Extreme Learning Machine“. Journal of Physics: Conference Series 1848, Nr. 1 (01.04.2021): 012095. http://dx.doi.org/10.1088/1742-6596/1848/1/012095.

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28

Xin Ma, Shengkai Zhou und Yibin Li. „Incremental Human Action Recognition with Online Sequential Extreme Learning Machine“. International Journal of Advancements in Computing Technology 5, Nr. 9 (31.05.2013): 155–63. http://dx.doi.org/10.4156/ijact.vol5.issue9.19.

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29

Wang, Xingbiao, Bin Gu, Quanyi Zou und Rui Lei. „Local Gravitation Clustering-Based Semisupervised Online Sequential Extreme Learning Machine“. Security and Communication Networks 2022 (11.05.2022): 1–15. http://dx.doi.org/10.1155/2022/1735573.

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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.

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30

Zhao, Zhongtang, Xuezhuan Zhao und Lingling Li. „Self labeling online sequential extreme learning machine and it’s application“. Journal of Intelligent & Fuzzy Systems 37, Nr. 4 (25.10.2019): 4485–91. http://dx.doi.org/10.3233/jifs-179281.

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31

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

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32

Cao, Weipeng, Jinzhu Gao, Zhong Ming, Shubin Cai und Zhiguang Shan. „Fuzziness-based online sequential extreme learning machine for classification problems“. Soft Computing 22, Nr. 11 (07.02.2018): 3487–94. http://dx.doi.org/10.1007/s00500-018-3021-4.

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33

Xie, Shan Juan, JuCheng Yang, Hui Gong, Sook Yoon und Dong Sun Park. „Intelligent fingerprint quality analysis using online sequential extreme learning machine“. Soft Computing 16, Nr. 9 (24.02.2012): 1555–68. http://dx.doi.org/10.1007/s00500-012-0828-2.

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34

Kale, Archana P., Sumedh Sonawane, Revati M. Wahul und Manisha A. Dudhedia. „Improved Genetic Optimized Feature Selection for Online Sequential Extreme Learning Machine“. Ingénierie des systèmes d information 27, Nr. 5 (31.10.2022): 843–48. http://dx.doi.org/10.18280/isi.270519.

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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.

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35

Yang, Rui, Yongbao Liu, Xing He und Zhimeng Liu. „Gas Turbine Model Identification Based on Online Sequential Regularization Extreme Learning Machine with a Forgetting Factor“. Energies 16, Nr. 1 (27.12.2022): 304. http://dx.doi.org/10.3390/en16010304.

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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.

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36

Zhou, Zhiyu, Jiangfei Ji, Yaming Wang, Zefei Zhu und 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, Nr. 3 (01.05.2022): 172988062211086. http://dx.doi.org/10.1177/17298806221108603.

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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.

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37

Peng, Xiuyan, Bo Wang, Lanyong Zhang und Peng Su. „Adaptive Online Sequential Extreme Learning Machine with Kernels for Online Ship Power Prediction“. Energies 14, Nr. 17 (29.08.2021): 5371. http://dx.doi.org/10.3390/en14175371.

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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.

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38

Bielskus, Jonas, Violeta Motuzienė, Tatjana Vilutienė und Audrius Indriulionis. „Occupancy Prediction Using Differential Evolution Online Sequential Extreme Learning Machine Model“. Energies 13, Nr. 15 (04.08.2020): 4033. http://dx.doi.org/10.3390/en13154033.

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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.

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39

Atsawaraungsuk, Sarutte, und Tatpong Katanyukul. „Sin Activation Structural Tolerance of Online Sequential Circular Extreme Learning Machine“. International Journal of Technology 8, Nr. 4 (31.07.2017): 601. http://dx.doi.org/10.14716/ijtech.v8i4.9476.

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40

AlDahoul, Nouar, und Zaw Zaw Htike. „Online Sequential Extreme Learning Machine Based Functional Magnetic Resonance Imaging Decoder“. Advanced Science Letters 21, Nr. 11 (01.11.2015): 3489–93. http://dx.doi.org/10.1166/asl.2015.6595.

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41

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

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42

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

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43

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

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44

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

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45

Jiang, Xinlong, Junfa Liu, Yiqiang Chen, Dingjun Liu, Yang Gu und Zhenyu Chen. „Feature Adaptive Online Sequential Extreme Learning Machine for lifelong indoor localization“. Neural Computing and Applications 27, Nr. 1 (26.09.2014): 215–25. http://dx.doi.org/10.1007/s00521-014-1714-x.

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46

Vong, Chi-Man, Weng-Fai Ip, Chi-Chong Chiu und Pak-Kin Wong. „Imbalanced Learning for Air Pollution by Meta-Cognitive Online Sequential Extreme Learning Machine“. Cognitive Computation 7, Nr. 3 (26.08.2014): 381–91. http://dx.doi.org/10.1007/s12559-014-9301-0.

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47

Zhou, Xinran, Zijian Liu und 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.

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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.

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Liu, Yang, Bo He, Diya Dong, Yue Shen, Tianhong Yan, Rui Nian und 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.

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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.

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Zhao, Feiyu, Buyun Sheng, Xiyan Yin, Hui Wang, Xincheng Lu und 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.

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Zhang, Yubin, Zhengying Wei, Lei Zhang, Qinyin Lin und Jun Du. „Improved online sequential extreme learning machine for simulation of daily reference evapotranspiration“. Tecnología y ciencias del agua 08, Nr. 2 (10.03.2017): 127–40. http://dx.doi.org/10.24850/j-tyca-2017-02-12.

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