Portada del libro de Advanced data-driven approaches for modelling and classification
Título del libro:

Advanced data-driven approaches for modelling and classification

with applications to automotive engine fault detection and polymer extrusion control

LAP LAMBERT Academic Publishing (2012-11-12 )

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ISBN-13:

978-3-659-30141-4

ISBN-10:
3659301418
EAN:
9783659301414
Idioma del libro:
Inglés
Notas y citas / Texto breve:
In this book, the Fast Recursive Algorithm (FRA) and Two-Stage Selection (TSS) methods proposed by Prof. Li and Prof. Irwin have been improved to integrate Bayesian regularisation to prevent over-fitting and leave-one-out cross validation for automatic model construction. To further enhance model generalization capability, some heuristic methods were also embedded in the two-stage selection to optimize the non-linear parameters involved in subset model construction. These include Particle Swarm Optimization (PSO), Defferential Evolution (DE), and Extreme Learning Machine (ELM). The effectiveness and efficiency of all these advanced methods have been confirmed on both well-known benchmarks and real world data sets from automotive engine and polymer extrusion applications.
Editorial:
LAP LAMBERT Academic Publishing
Sitio web:
https://www.lap-publishing.com/
Por (autor):
Jing Deng
Número de páginas:
160
Publicado en:
2012-11-12
Stock:
Disponible
Categoría:
Electrónica, electrotécnica, la tecnología de las comunicaciones
Precio:
59.00 €
Palabras clave:
PSO, De, Bayesian learning, Elm, Nonlinear System Identification, Subset selection, RBF network, Leave-one-out cross validation

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