Randomized algorithms for nonlinear system identification with deep learning modification
Both randomized algorithms and deep learning techniques have been successfully used for regression and classification problems. However, the random hidden weights of the randomized algorithms require suitable distributions in advance, and the deep learning methods do not use the output information i...
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| Vydáno v: | Information sciences Ročník 364-365; s. 197 - 212 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Inc
01.10.2016
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| ISSN: | 0020-0255, 1872-6291 |
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| Abstract | Both randomized algorithms and deep learning techniques have been successfully used for regression and classification problems. However, the random hidden weights of the randomized algorithms require suitable distributions in advance, and the deep learning methods do not use the output information in system identification.
In this paper, the distributions of the hidden weights are obtained by the restricted Boltzmann machines. This deep learning method uses input data to construct the statistical features of the hidden weights. The output weights of the neural model are trained by normal randomized algorithms. So we successfully combine the unsupervised training (deep learning) and the supervised learning method (randomized algorithm), and take advantages from both of them. The proposed randomized algorithms with deep learning modification are validated with three benchmark problems. |
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| AbstractList | Both randomized algorithms and deep learning techniques have been successfully used for regression and classification problems. However, the random hidden weights of the randomized algorithms require suitable distributions in advance, and the deep learning methods do not use the output information in system identification. In this paper, the distributions of the hidden weights are obtained by the restricted Boltzmann machines. This deep learning method uses input data to construct the statistical features of the hidden weights. The output weights of the neural model are trained by normal randomized algorithms. So we successfully combine the unsupervised training (deep learning) and the supervised learning method (randomized algorithm), and take advantages from both of them. The proposed randomized algorithms with deep learning modification are validated with three benchmark problems. Both randomized algorithms and deep learning techniques have been successfully used for regression and classification problems. However, the random hidden weights of the randomized algorithms require suitable distributions in advance, and the deep learning methods do not use the output information in system identification. In this paper, the distributions of the hidden weights are obtained by the restricted Boltzmann machines. This deep learning method uses input data to construct the statistical features of the hidden weights. The output weights of the neural model are trained by normal randomized algorithms. So we successfully combine the unsupervised training (deep learning) and the supervised learning method (randomized algorithm), and take advantages from both of them. The proposed randomized algorithms with deep learning modification are validated with three benchmark problems. |
| Author | Yu, Wen de la Rosa, Erick |
| Author_xml | – sequence: 1 givenname: Erick surname: de la Rosa fullname: de la Rosa, Erick – sequence: 2 givenname: Wen orcidid: 0000-0002-9540-7924 surname: Yu fullname: Yu, Wen email: yuw@ctrl.cinvestav.mx |
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| Cites_doi | 10.1016/j.conengprac.2012.08.009 10.1162/neco.2008.11-07-647 10.1109/TNN.2002.804317 10.1109/TNNLS.2012.2236572 10.1109/TNN.2011.2168423 10.1207/s15516709cog0901_7 10.1109/21.256541 10.1109/TNNLS.2014.2305760 10.1109/72.846746 10.1109/TFUZZ.1993.390281 10.1109/72.471375 10.1016/S0005-1098(96)80007-0 10.1109/72.80336 10.1142/S0129065710002516 10.1109/TNN.2003.811356 10.1137/0111030 10.1016/j.neunet.2013.02.008 10.1109/3477.484441 10.1016/j.ins.2013.12.016 |
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| Title | Randomized algorithms for nonlinear system identification with deep learning modification |
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