ANN based modeling, testing and diagnosis of MEMS
ANN based modeling, testing and diagnosis of MEMS
New concepts of simulation, testing and diagnosis of MEMS are proposed, intended to boost the time to market and dependability of such systems. Black-box modeling of non-electronic parts is introduced using artificial neural networks, so enabling radically faster simulation without concurrent algorithms and parallel computation. A Jumped model of the capacitive transducer, being the part of a micro-electro-mechanical capacitive pressure sensing system, is created using an ANN. Faults are then introduced to the sensing system and simulation of the fault-free and faulty circuits are demonstrated.
0-7803-8547-0
183-188
Litovski, V
6d93668f-3784-453f-bd36-f9d3d7bdd9a5
Andrejevic, M
bb6a56a9-c5e2-45ac-ae08-ea194d3d2983
Zwolinski, M
adfcb8e7-877f-4bd7-9b55-7553b6cb3ea0
2004
Litovski, V
6d93668f-3784-453f-bd36-f9d3d7bdd9a5
Andrejevic, M
bb6a56a9-c5e2-45ac-ae08-ea194d3d2983
Zwolinski, M
adfcb8e7-877f-4bd7-9b55-7553b6cb3ea0
Litovski, V, Andrejevic, M and Zwolinski, M
(2004)
ANN based modeling, testing and diagnosis of MEMS.
NEUREL 2004: SEVENTH SEMINAR ON NEURAL NETWORK APPLICATIONS IN ELECTRICAL ENGINEERING, Belgrade, Serbia.
22 - 24 Sep 2004.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
New concepts of simulation, testing and diagnosis of MEMS are proposed, intended to boost the time to market and dependability of such systems. Black-box modeling of non-electronic parts is introduced using artificial neural networks, so enabling radically faster simulation without concurrent algorithms and parallel computation. A Jumped model of the capacitive transducer, being the part of a micro-electro-mechanical capacitive pressure sensing system, is created using an ANN. Faults are then introduced to the sensing system and simulation of the fault-free and faulty circuits are demonstrated.
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More information
Published date: 2004
Additional Information:
Event Dates: SEP 23-25, 2004
Venue - Dates:
NEUREL 2004: SEVENTH SEMINAR ON NEURAL NETWORK APPLICATIONS IN ELECTRICAL ENGINEERING, Belgrade, Serbia, 2004-09-22 - 2004-09-24
Organisations:
EEE
Identifiers
Local EPrints ID: 263413
URI: http://eprints.soton.ac.uk/id/eprint/263413
ISBN: 0-7803-8547-0
PURE UUID: 60e81b49-bed7-4c07-bbea-df19722a56d7
Catalogue record
Date deposited: 12 Feb 2007
Last modified: 09 Jan 2022 02:36
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Contributors
Author:
V Litovski
Author:
M Andrejevic
Author:
M Zwolinski
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