Parallel Approach for Time Series Analysis with General Regression Neural Networks
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| Format: | Artículo científico |
| Langue: | en |
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Universidad Nacional Autónoma de México
2012
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| _version_ | 1876488237631930368 |
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| author | J.C. Cuevas-Tello |
| author_facet | J.C. Cuevas-Tello |
| contents | Parallel Approach for Time Series Analysis with General Regression Neural Networks J.C. Cuevas-Tello R.A. González-Grimaldo O. Rodríguez-González H.G. Pérez-González O. Vital-Ochoa Ingeniería time series Neural networks machine learning parallel algorithms The accuracy on time delay estimation given pairs of irregularly sampled time series is of great relevance inastrophysics. However the computational time is also important because the study of large data sets is needed. Besides introducing a new approach for time delay estimation, this paper presents a parallel approach to obtain a fast algorithm for time delay estimation. The neural network architecture that we use is general Regression Neural Network (GRNN). For the parallel approach, we use Message Passing Interface (MPI) on a beowulf-type cluster and on a Cray supercomputer and we also use the Compute Unified Device Architecture (CUDA¿)language on Graphics Processing Units (GPUs). We demonstrate that, with our approach, fast algorithms can be obtained for time delay estimation on large data sets with the same accuracy as state of-the-art methods. 2012 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47423215008 en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.2 Vol.10 |
| format | Artículo científico |
| id | redalyc_47423215008 |
| institution | Redalyc |
| language | en |
| publishDate | 2012 |
| publisher | Universidad Nacional Autónoma de México |
| spellingShingle | Parallel Approach for Time Series Analysis with General Regression Neural Networks J.C. Cuevas-Tello Ingeniería time series Neural networks machine learning parallel algorithms Parallel Approach for Time Series Analysis with General Regression Neural Networks J.C. Cuevas-Tello R.A. González-Grimaldo O. Rodríguez-González H.G. Pérez-González O. Vital-Ochoa Ingeniería time series Neural networks machine learning parallel algorithms The accuracy on time delay estimation given pairs of irregularly sampled time series is of great relevance inastrophysics. However the computational time is also important because the study of large data sets is needed. Besides introducing a new approach for time delay estimation, this paper presents a parallel approach to obtain a fast algorithm for time delay estimation. The neural network architecture that we use is general Regression Neural Network (GRNN). For the parallel approach, we use Message Passing Interface (MPI) on a beowulf-type cluster and on a Cray supercomputer and we also use the Compute Unified Device Architecture (CUDA¿)language on Graphics Processing Units (GPUs). We demonstrate that, with our approach, fast algorithms can be obtained for time delay estimation on large data sets with the same accuracy as state of-the-art methods. 2012 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47423215008 en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.2 Vol.10 |
| title | Parallel Approach for Time Series Analysis with General Regression Neural Networks |
| topic | Ingeniería time series Neural networks machine learning parallel algorithms |
| url | https://www.redalyc.org/articulo.oa?id=47423215008 |