Wavelet entropy of stochastic processes
We compare two different definitions for the wavelet entropy associated to stochastic processes. The first one, the normalized total wavelet entropy (NTWS) family [S. Blanco, A. Figliola, R.Q. Quiroga, O.A. Rosso, E. Serrano, Time–frequency analysis of electroencephalogram series, III. Wavelet packe...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2007 |
| País: | Argentina |
| Institución: | Universidad Nacional de La Plata |
| Repositorio: | SEDICI (UNLP) |
| Idioma: | inglés |
| OAI Identifier: | oai:sedici.unlp.edu.ar:10915/131070 |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/131070 |
| Access Level: | acceso abierto |
| Palabra clave: | Física Wavelet analysis Wavelet entropy Fractional Brownian motion Fractional Gaussian noise α-parameter |
| Sumario: | We compare two different definitions for the wavelet entropy associated to stochastic processes. The first one, the normalized total wavelet entropy (NTWS) family [S. Blanco, A. Figliola, R.Q. Quiroga, O.A. Rosso, E. Serrano, Time–frequency analysis of electroencephalogram series, III. Wavelet packets and information cost function, Phys. Rev. E 57 (1998) 932–940; O.A. Rosso, S. Blanco, J. Yordanova, V. Kolev, A. Figliola, M. Schürmann, E. Başar, Wavelet entropy: a new tool for analysis of short duration brain electrical signals, J. Neurosci. Method 105 (2001) 65–75] and a second introduced by Tavares and Lucena [Physica A 357(1) (2005) 71–78]. In order to understand their advantages and disadvantages, exact results obtained for fractional Gaussian noise (<math><mo is="true">-</mo><mn is="true">1</mn><mo is="true"><</mo><mi is="true">α</mi><mo is="true"><</mo><mspace width="0.33em" is="true"></mspace><mn is="true">1</mn></math>) and fractional Brownian motion (<math><mn is="true">1</mn><mo is="true"><</mo><mi is="true">α</mi><mo is="true"><</mo><mspace width="0.33em" is="true"></mspace><mn is="true">3</mn></math>) are assessed. We find out that the NTWS family performs better as a characterization method for these stochastic processes. |
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