Catalogo Articoli (Spogli Riviste)

OPAC HELP

Titolo:
A neural-network-based method of model reduction for the dynamic simulation of MEMS
Autore:
Liang, YC; Lin, WZ; Lee, HP; Lim, SP; Lee, KH; Feng, DP;
Indirizzi:
Jilin Univ, Dept Comp Sci, Seoul 130012, South Korea Jilin Univ Seoul South Korea 130012 Comp Sci, Seoul 130012, South Korea Natl Univ Singapore, Dept Mech Engn, Ctr Adv Computat Engn Sci, Singapore 119260, Singapore Natl Univ Singapore Singapore Singapore 119260 ngapore 119260, Singapore Natl Univ Singapore, Dept Mech Engn, Singapore 119260, Singapore Natl UnivSingapore Singapore Singapore 119260 ngapore 119260, Singapore Inst High Performance Comp, Singapore 118261, Singapore Inst High Performance Comp Singapore Singapore 118261 118261, Singapore Jilin Univ, Dept Math, Seoul 130012, South Korea Jilin Univ Seoul South Korea 130012 Dept Math, Seoul 130012, South Korea
Titolo Testata:
JOURNAL OF MICROMECHANICS AND MICROENGINEERING
fascicolo: 3, volume: 11, anno: 2001,
pagine: 226 - 233
SICI:
0960-1317(200105)11:3<226:ANMOMR>2.0.ZU;2-W
Fonte:
ISI
Lingua:
ENG
Soggetto:
COMPUTER-AIDED GENERATION; MACROMODELS;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
20
Recensione:
Indirizzi per estratti:
Indirizzo: Liang, YC Jilin Univ, Dept Comp Sci, 10 Qian Wei Rd, Seoul 130012, South Korea Jilin Univ 10 Qian Wei Rd Seoul South Korea 130012 South Korea
Citazione:
Y.C. Liang et al., "A neural-network-based method of model reduction for the dynamic simulation of MEMS", J MICROM M, 11(3), 2001, pp. 226-233

Abstract

This paper proposes a neuro-network-based method for model reduction that combines the generalized Hebbian algorithm (GHA) with the Galerkin procedure to perform the dynamic simulation and analysis of nonlinear microelectromechanical systems (MEMS). An unsupervised neural network is adopted to findthe principal eigenvectors of a correlation matrix of snapshots. It has been shown that the extensive computer results of the principal component analysis using the neural network of GHA can extract an empirical basis from numerical or experimental data, which can be used to convert the original system into a lumped low-order macromodel, The macromodel can be employed to carry out the dynamic simulation of the original system resulting in a dramatic reduction of computation time while not losing flexibility and accuracy. Compared with other existing model reduction methods for the dynamic simulation of MEMS, the present method does not need to compute the input correlation matrix in advance. It needs only to find very few required basis functions, which can be learned directly from the input data, and this means that the method possesses potential advantages when the measured data are large. The method is evaluated to simulate the pull-in dynamics of a doubly-clamped microbeam subjected to different input voltage spectra of electrostatic actuation. The efficiency and the flexibility of the proposed method are examined by comparing the results with those of the fully meshed finite-difference method.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 04/04/20 alle ore 02:28:03