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The Scientific World Journal
Volume 2014 (2014), Article ID 906546, 7 pages
http://dx.doi.org/10.1155/2014/906546
Research Article

An Improved Kernel Based Extreme Learning Machine for Robot Execution Failures

1School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China
2School of Science, Qilu University of Technology, Jinan, Shandong 250353, China

Received 8 February 2014; Accepted 18 March 2014; Published 8 April 2014

Academic Editors: S. Balochian and V. Bhatnagar

Copyright © 2014 Bin Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [8 citations]

The following is the list of published articles that have cited the current article.

  • Ahmad Mozaffari, and Nasser L. Azad, “A Soft Sensor Based on the Integration of Tikhonov Extreme Learning Machine and Accelerated Kernels for Real-Time Estimation of Automotive Catalyst Temperatures,” International Journal of Computational Intelligence and Applications, vol. 14, no. 4, 2015. View at Publisher · View at Google Scholar
  • Siyuan Lu, Zhihai Lu, Jianfei Yang, Ming Yang, and Shuihua Wang, “A pathological brain detection system based on kernel based ELM,” Multimedia Tools and Applications, vol. 77, no. 3, pp. 3715–3728, 2016. View at Publisher · View at Google Scholar
  • Derya Avci, and Akif Dogantekin, “An Expert Diagnosis System for Parkinson Disease Based on Genetic Algorithm-Wavelet Kernel-Extreme Learning Machine,” Parkinson's Disease, vol. 2016, pp. 1–9, 2016. View at Publisher · View at Google Scholar
  • Senyue Zhang, and Wenan Tan, “An Extreme Learning Machine Based on the Mixed Kernel Function of Triangular Kernel and Generalized Hermite Dirichlet Kernel,” Discrete Dynamics in Nature and Society, vol. 2016, 2016. View at Publisher · View at Google Scholar
  • Zhang Yang, Li Ce, and Li Lian, “Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods,” Applied Energy, vol. 190, pp. 291–305, 2017. View at Publisher · View at Google Scholar
  • Dejey Dharma, and A. Sherly Alphonse, “Enhanced Gabor (E-Gabor), Hypersphere-based normalization and Pearson General Kernel-based discriminant analysis for dimension reduction and classification of facial emotions,” Expert Systems with Applications, vol. 90, pp. 127–145, 2017. View at Publisher · View at Google Scholar
  • Dongrae Cho, Jinsil Ham, Jooyoung Oh, Jeanho Park, Sayup Kim, Nak-Kyu Lee, and Boreom Lee, “Detection of Stress Levels from Biosignals Measured in Virtual Reality Environments Using a Kernel-Based Extreme Learning Machine,” Sensors, vol. 17, no. 11, pp. 2435, 2017. View at Publisher · View at Google Scholar
  • Fatih Ertam, and Engin Avcı, “A new approach for internet traffic classification: GA-WK-ELM,” Measurement: Journal of the International Measurement Confederation, vol. 95, pp. 135–142, 2017. View at Publisher · View at Google Scholar