ELM in nonstationary environment | Extreme Learning Machine and its variants for Time-Varying Neural Networks case study. Este artículo no está disponible.
Idioma: inglés
Editorial: LAP LAMBERT Academic Publishing, 2014
- Tapa blanda
- Nuevo
Librería: preigu, Osnabrück, Alemaniapreigu
Vendedor de 5 estrellas
Vendedor de AbeBooks desde 5 de agosto de 2024
No disponible
Tapa blanda
Condición: Nuevo
EUR 196,00
Descripción del artículo del vendedor
ELM in nonstationary environment | Extreme Learning Machine and its variants for Time-Varying Neural Networks case study | Yibin Ye (u. a.) | Taschenbuch | 88 S. | Englisch | 2014 | LAP LAMBERT Academic Publishing | EAN 9783659248900 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
N° de ref. del artículo 106175482
- Título
- ELM in nonstationary environment | Extreme Learning Machine and its variants for Time-Varying Neural Networks case study
- Autor
- Yibin Ye (u. a.)
- Editorial
- LAP LAMBERT Academic Publishing
- Año de publicación
- 2014
- Estado
- Neu
- Encuadernación
- Taschenbuch
- Idioma
- inglés
- ISBN 10
- 3659248908
- ISBN 13
- 9783659248900
- Peso del artículo
- 149 gramos
- Dimensiones
- 220 x 150 x 6 mm
- Catálogos de vendedores
- Bücher
System identification in nonstationary environment represents a challenging problem and an advaned neural architecture namely Time-Varying Neural Net- works (TV-NN) has shown remarkable identification properties in nonlinear and nonstationary conditions. Time-varying weights, each being a linear com- bination of a certain set of basis functions, are used in such kind of networks instead of stable ones, which inevitalbly increases the number of free parame- ters. Therefore, an Extreme Learning Machine (ELM) approach is developed to accelerate the training procedure for TV-NN. What is more, in order to ob- tain a more compact structure, or determine several important parameters, or update the network more efficiently in online case, several variants of ELM-TV are proposed and discussed in the book. Related computer simulations have been carried out and show the effectiveness of the algorithms.
“Sinopsis” puede pertenecer a otra edición de este título.
Reseña del editor
System identification in nonstationary environment represents a challenging problem and an advaned neural architecture namely Time-Varying Neural Net- works (TV-NN) has shown remarkable identification properties in nonlinear and nonstationary conditions. Time-varying weights, each being a linear com- bination of a certain set of basis functions, are used in such kind of networks instead of stable ones, which inevitalbly increases the number of free parame- ters. Therefore, an Extreme Learning Machine (ELM) approach is developed to accelerate the training procedure for TV-NN. What is more, in order to ob- tain a more compact structure, or determine several important parameters, or update the network more efficiently in online case, several variants of ELM-TV are proposed and discussed in the book. Related computer simulations have been carried out and show the effectiveness of the algorithms.
“Acerca de” puede pertenecer a otra edición de este título.