Thematische Bibliographien / ONLINE SEQUENTIAL FUZZY EXTREME LEARNING MACHINE

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  1. Zeitschriftenartikel
  2. Dissertationen
  3. Buchteile
  4. Konferenzberichte

Auswahl der wissenschaftlichen Literatur zum Thema „ONLINE SEQUENTIAL FUZZY EXTREME LEARNING MACHINE“

Autor: Grafiati
Veröffentlicht am 11. September 2023

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Zeitschriftenartikel zum Thema "ONLINE SEQUENTIAL FUZZY EXTREME LEARNING MACHINE"

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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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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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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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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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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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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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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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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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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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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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Dissertationen zum Thema "ONLINE SEQUENTIAL FUZZY EXTREME LEARNING MACHINE"

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BHATNAGAR, AKHILESH CHANDRA. „MODIFIED ONLINE SEQUENTIAL FUZZY EXTREME LEARNING MACHINE“. Thesis, 2011. http://dspace.dtu.ac.in:8080/jspui/handle/repository/13869.

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M.TECH
This report addresses modification for recently developed sequential learning algorithm (OS-Fuzzy ELM ) and its performance evaluation is done using multi-category classification Data Sets of VC, GI and IS and Binary classification data sets like liver disorder from UCI. There are two main sections to the report. The first of these is the presentation of research gathered on fuzzy neural networks and the possible purpose they could serve in communications, as well as giving background information on the individual disciplines. The second half of the report is concerned with Modified OS-Fuzzy ELM algorithm and its performance evaluation and comparison of results with recently developed sequential learning algorithm for Self-adaptive Re- source Allocation Network classifier ( SRAN).
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Cheng, Yu-Yuan, und 鄭育淵. „Online Fuzzy Extreme Learning Machine Based on Recursive Singular Value Decomposition“. Thesis, 2017. http://ndltd.ncl.edu.tw/handle/957pjj.

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碩士
義守大學
資訊工程學系
105
In this study, we propose an online fuzzy extreme learning machine based on the recursive singular value decomposition for improving the fuzzy extreme learning machine, and therefore making it applicable for solving online learning problems in classification or regression modeling. Like the original fuzzy extreme learning machine, our approach randomly assigns values to weights of fuzzy membership functions in the hidden layer. However, the Moore-Penrose pseudoinverse is replaced with the recursive singular value decomposition for calculating the optimal weights corresponding to the output layer. Compared with the original fuzzy extreme learning machine, our approach is applicable for the online learning of classification or regression modeling and produces the same modeling accuracy. Moreover, our approach possesses the better modeling accuracy and stability than the other approach, namely, online sequential learning algorithm.
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Buchteile zum Thema "ONLINE SEQUENTIAL FUZZY EXTREME LEARNING MACHINE"

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Yin, Jianchuan, und Nini Wang. „An Online Sequential Extreme Learning Machine for Tidal Prediction Based on Improved Gath-Geva Fuzzy Segmentation“. In Proceedings in Adaptation, Learning and Optimization, 243–52. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14066-7_24.

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Hoang, Minh-Tuan T., Hieu T. Huynh, Nguyen H. Vo und Yonggwan Won. „A Robust Online Sequential Extreme Learning Machine“. In Advances in Neural Networks – ISNN 2007, 1077–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72383-7_126.

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Singh, Ram Pal, Neelam Dabas, Vikash Chaudhary und Nagendra. „Online Sequential Extreme Learning Machine for Watermarking“. In Proceedings in Adaptation, Learning and Optimization, 115–24. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14066-7_12.

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Zhao, Zhongtang, Li Liu, Lingling Li und Qian Ma. „SLOSELM: Self Labeling Online Sequential Extreme Learning Machine“. In Internet and Distributed Computing Systems, 179–89. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45940-0_16.

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Jia, Xibin, Runyuan Wang, Junfa Liu und David M. W. Powers. „A Semi-supervised Online Sequential Extreme Learning Machine Method“. In Proceedings of ELM-2014 Volume 1, 301–10. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14063-6_26.

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Mirza, Bilal, Stanley Kok und Fei Dong. „Multi-layer Online Sequential Extreme Learning Machine for Image Classification“. In Proceedings of ELM-2015 Volume 1, 39–49. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28397-5_4.

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Huang, Shan, Botao Wang, Junhao Qiu, Jitao Yao, Guoren Wang und Ge Yu. „Parallel Ensemble of Online Sequential Extreme Learning Machine Based on MapReduce“. In Proceedings of ELM-2014 Volume 1, 31–40. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14063-6_3.

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Yin, Jianchuan, Lianbo Li, Yuchi Cao und Jian Zhao. „An Adaptive Online Sequential Extreme Learning Machine for Real-Time Tidal Level Prediction“. In Proceedings in Adaptation, Learning and Optimization, 55–66. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28373-9_5.

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Huang, Shan, Botao Wang, Yuemei Chen, Guoren Wang und Ge Yu. „Efficient Batch Parallel Online Sequential Extreme Learning Machine Algorithm Based on MapReduce“. In Proceedings of ELM-2015 Volume 1, 13–25. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28397-5_2.

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Xu, Xiaoming, Chenglin Wen, Weijie Chen und Siyu Ji. „The Parameter Updating Method Based on Kalman Filter for Online Sequential Extreme Learning Machine“. In Proceedings in Adaptation, Learning and Optimization, 80–102. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01520-6_8.

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Konferenzberichte zum Thema "ONLINE SEQUENTIAL FUZZY EXTREME LEARNING MACHINE"

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Yu Jun und Meng Joo Er. „An Enhanced Online Sequential Extreme Learning Machine algorithm“. In 2008 Chinese Control and Decision Conference (CCDC). IEEE, 2008. http://dx.doi.org/10.1109/ccdc.2008.4597855.

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Zhang, Senyue, Wenan Tan und Yibo Li. „A Survey of Online Sequential Extreme Learning Machine“. In 2018 5th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2018. http://dx.doi.org/10.1109/codit.2018.8394791.

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Chacko, B. P., und A. P. Babu. „Online sequential extreme learning machine based handwritten character recognition“. In 2011 IEEE Students' Technology Symposium (TechSym). IEEE, 2011. http://dx.doi.org/10.1109/techsym.2011.5783843.

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Yuan Lan, Yeng Chai Soh und Guang-Bin Huang. „A constructive enhancement for Online Sequential Extreme Learning Machine“. In 2009 International Joint Conference on Neural Networks (IJCNN 2009 - Atlanta). IEEE, 2009. http://dx.doi.org/10.1109/ijcnn.2009.5178608.

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Rau, Francisco, Ismael Soto, Pablo Adasme, David Zabala-Blanco und Cesar A. Azurdia-Meza. „Network Traffic Prediction Using Online-Sequential Extreme Learning Machine“. In 2021 Third South American Colloquium on Visible Light Communications (SACVLC). IEEE, 2021. http://dx.doi.org/10.1109/sacvlc53127.2021.9652247.

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Chen, Yi-Ta, Yu-Chuan Chuang und An-Yeu Andy Wu. „AdaBoost-assisted Extreme Learning Machine for Efficient Online Sequential Classification“. In 2019 IEEE International Workshop on Signal Processing Systems (SiPS). IEEE, 2019. http://dx.doi.org/10.1109/sips47522.2019.9020609.

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Lu, Siyuan, Hainan Wang, Xueyan Wu und Shuihua Wang. „Pathological brain detection based on online sequential extreme learning machine“. In 2016 International Conference on Progress in Informatics and Computing (PIC). IEEE, 2016. http://dx.doi.org/10.1109/pic.2016.7949498.

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Liu, Ye, Weipeng Cao, Yiwen Liu, Dachuan Li und Qiang Wang. „Ensemble Online Sequential Extreme Learning Machine for Air Quality Prediction“. In 2021 IEEE 7th International Conference on Control Science and Systems Engineering (ICCSSE). IEEE, 2021. http://dx.doi.org/10.1109/iccsse52761.2021.9545089.

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Liu, Zongying, und Kitsuchart Pasupa. „Online Sequential Extreme Learning Machine based Instinct Plasticity for Classification“. In 2020 12th International Conference on Information Technology and Electrical Engineering (ICITEE). IEEE, 2020. http://dx.doi.org/10.1109/icitee49829.2020.9271686.

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Maliha, Ayman, Rubiyah Yusof und Ahmed Madani. „Online sequential-extreme learning machine based detector on training-learning-detection framework“. In 2015 10th Asian Control Conference (ASCC). IEEE, 2015. http://dx.doi.org/10.1109/ascc.2015.7244867.

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