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Artificial Neural Networks in Vehicular Pollution Modelling
Artificial neural networks (ANNs), which are parallel computational models, comprising of interconnected adaptive processing units (neurons) have the capability to predict accurately the dispersive behavior of vehicular pollutants under complex environmental conditions. This book aims at describing...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2007.
|
| Edición: | 1st ed. 2007. |
| Colección: | Studies in Computational Intelligence,
41 |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | 978-3-540-37418-3 | ||
| 003 | DE-He213 | ||
| 005 | 20220116185311.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 100301s2007 gw | s |||| 0|eng d | ||
| 020 | |a 9783540374183 |9 978-3-540-37418-3 | ||
| 024 | 7 | |a 10.1007/978-3-540-37418-3 |2 doi | |
| 050 | 4 | |a TA329-348 | |
| 050 | 4 | |a TA345-345.5 | |
| 072 | 7 | |a TBJ |2 bicssc | |
| 072 | 7 | |a TEC009000 |2 bisacsh | |
| 072 | 7 | |a TBJ |2 thema | |
| 082 | 0 | 4 | |a 620 |2 23 |
| 100 | 1 | |a Khare, Mukesh. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Artificial Neural Networks in Vehicular Pollution Modelling |h [electronic resource] / |c by Mukesh Khare, S.M. Shiva Nagendra. |
| 250 | |a 1st ed. 2007. | ||
| 264 | 1 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg : |b Imprint: Springer, |c 2007. | |
| 300 | |a XVI, 242 p. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Studies in Computational Intelligence, |x 1860-9503 ; |v 41 | |
| 505 | 0 | |a Vehicular Pollution -- Artificial Neutral Networks -- Vehicular Pollution Modelling-Conventional Aproach -- Vehicular Pollution Modelling -ANN Aproach -- Aplication of ANN based Vehicular Pollution Models -- Epilogue. | |
| 520 | |a Artificial neural networks (ANNs), which are parallel computational models, comprising of interconnected adaptive processing units (neurons) have the capability to predict accurately the dispersive behavior of vehicular pollutants under complex environmental conditions. This book aims at describing step-by-step procedure for formulation and development of ANN based VP models considering meteorological and traffic parameters. The model predictions are compared with existing line source deterministic/statistical based models to establish the efficacy of the ANN technique in explaining frequent dispersion complexities in urban areas. The book is very useful for hardcore professionals and researchers working in problems associated with urban air pollution management and control. | ||
| 650 | 0 | |a Engineering mathematics. | |
| 650 | 0 | |a Engineering-Data processing. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Automotive engineering. | |
| 650 | 0 | |a Pollution. | |
| 650 | 0 | |a Mathematics. | |
| 650 | 0 | |a Computational intelligence. | |
| 650 | 1 | 4 | |a Mathematical and Computational Engineering Applications. |
| 650 | 2 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Automotive Engineering. |
| 650 | 2 | 4 | |a Pollution. |
| 650 | 2 | 4 | |a Applications of Mathematics. |
| 650 | 2 | 4 | |a Computational Intelligence. |
| 700 | 1 | |a Nagendra, S.M. Shiva. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783642072222 |
| 776 | 0 | 8 | |i Printed edition: |z 9783540827849 |
| 776 | 0 | 8 | |i Printed edition: |z 9783540374176 |
| 830 | 0 | |a Studies in Computational Intelligence, |x 1860-9503 ; |v 41 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-540-37418-3 |z Texto Completo |
| 912 | |a ZDB-2-ENG | ||
| 912 | |a ZDB-2-SXE | ||
| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||