Wavelet-network based on L1-norm minimisation for learning chaotic time series
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| Formato: | Artículo científico |
| Lenguaje: | en |
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Universidad Nacional Autónoma de México
2005
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| _version_ | 1876456662843260928 |
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| author | V. Alarcon Aquino |
| author_facet | V. Alarcon Aquino |
| contents | Wavelet-network based on L1-norm minimisation for learning chaotic time series V. Alarcon Aquino E. S. Garcia Treviño R. Rosas Romero J. F. Ramirez Cruz L. G. Guerrero Ojeda J. Rodriguez Asomoza Ingeniería Multi Wavelet networks Wavelets resolution Analysis This paper presents a wavelet-neural network based on the L1-norm minimisation for learning chaotic time series.The proposed approach, which is based on multi-resolution analysis, uses wavelets as activation functions in thehidden layer of the wavelet-network. We propose using the L1-norm, as opposed to the L2-norm, due to the wellknownfact that the L1-norm is superior to the L2-norm criterion when the signal has heavy tailed distributions oroutliers. A comparison of the proposed approach with previous reported schemes using a time series benchmark ispresented. Simulation results show that the proposed wavelet-network based on the L1-norm performs better thanthe standard back-propagation network and the wavelet-network based on the traditional L2-norm when applied tosynthetic data. 2005 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47430305 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.3 Vol.3 |
| format | Artículo científico |
| id | redalyc_47430305 |
| institution | Redalyc |
| language | en |
| publishDate | 2005 |
| publisher | Universidad Nacional Autónoma de México |
| spellingShingle | Wavelet-network based on L1-norm minimisation for learning chaotic time series V. Alarcon Aquino Ingeniería Multi Wavelet networks Wavelets resolution Analysis Wavelet-network based on L1-norm minimisation for learning chaotic time series V. Alarcon Aquino E. S. Garcia Treviño R. Rosas Romero J. F. Ramirez Cruz L. G. Guerrero Ojeda J. Rodriguez Asomoza Ingeniería Multi Wavelet networks Wavelets resolution Analysis This paper presents a wavelet-neural network based on the L1-norm minimisation for learning chaotic time series.The proposed approach, which is based on multi-resolution analysis, uses wavelets as activation functions in thehidden layer of the wavelet-network. We propose using the L1-norm, as opposed to the L2-norm, due to the wellknownfact that the L1-norm is superior to the L2-norm criterion when the signal has heavy tailed distributions oroutliers. A comparison of the proposed approach with previous reported schemes using a time series benchmark ispresented. Simulation results show that the proposed wavelet-network based on the L1-norm performs better thanthe standard back-propagation network and the wavelet-network based on the traditional L2-norm when applied tosynthetic data. 2005 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47430305 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.3 Vol.3 |
| title | Wavelet-network based on L1-norm minimisation for learning chaotic time series |
| topic | Ingeniería Multi Wavelet networks Wavelets resolution Analysis |
| url | https://www.redalyc.org/articulo.oa?id=47430305 |