Buchcover von Advanced data-driven approaches for modelling and classification
Buchtitel:

Advanced data-driven approaches for modelling and classification

with applications to automotive engine fault detection and polymer extrusion control

LAP LAMBERT Academic Publishing (12.11.2012 )

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ISBN-13:

978-3-659-30141-4

ISBN-10:
3659301418
EAN:
9783659301414
Buchsprache:
Englisch
Klappentext:
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.
Verlag:
LAP LAMBERT Academic Publishing
Webseite:
https://www.lap-publishing.com/
von (Autor):
Jing Deng
Seitenanzahl:
160
Veröffentlicht am:
12.11.2012
Lagerbestand:
Lieferbar
Kategorie:
Elektronik, Elektrotechnik, Nachrichtentechnik
Preis:
59,00 €
Stichworte:
PSO, De, Bayesian learning, Elm, Nonlinear System Identification, Subset selection, RBF network, Leave-one-out cross validation

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