In this dissertation, suggestions and innovative solutions are proposed to fully perform an on-line optimization without any risk for the hardware. On the one hand, a new real-time fitness implementation can halt the carrying out of an experiment, if a highly unsatisfactory solution is recognized. On the other hand, a new hybrid architecture integrates EA and simplex method in order speed up the convergence.
Online hybrid evolutionary algorithms for auto-tuning of electric drives
Cascella, Giuseppe Leonardo
2005
Abstract
In this dissertation, suggestions and innovative solutions are proposed to fully perform an on-line optimization without any risk for the hardware. On the one hand, a new real-time fitness implementation can halt the carrying out of an experiment, if a highly unsatisfactory solution is recognized. On the other hand, a new hybrid architecture integrates EA and simplex method in order speed up the convergence.File in questo prodotto:
| File | Dimensione | Formato | |
|---|---|---|---|
|
D_2005_01.pdf
accesso aperto
Dimensione
2.26 MB
Formato
Adobe PDF
|
2.26 MB | Adobe PDF | Visualizza/Apri |
I documenti in UNITESI sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
Utilizza questo identificativo per citare o creare un link a questo documento:
https://hdl.handle.net/20.500.14242/64012
Il codice NBN di questa tesi è
URN:NBN:IT:POLIBA-64012