Developing smart micromachined transducers using feed-forward neural networks: A system identification and control perspective

E.I. Gaura; R.J. Rider; N. Steele

Developing smart micromachined transducers using feed-forward neural networks: A system identification and control perspective

E.I. Gaura, R.J. Rider, N. Steele

College of Engineering, Environment & Science

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

4 Citations (Scopus)

Abstract

The application of feedforward neural networks in micromachined acceleration sensors are discussed. Static and dynamic sensor identification is performed to improve the performance of neural, open- and closed-loop transducers. Simulation of the performance of the transducers indicated an extended measurement range compared to the `off-the-shelf' sensors.

Original language	English
Title of host publication	Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000. IJCNN 2000
Publisher	IEEE
Pages	353-358
Number of pages	6
ISBN (Print)	0-7695-0619-4
Publication status	Published - 2000
Event	International Joint Conference on Neural Networks: Neural Computing: New Challenges and Perspectives for the New Millennium - Como, Italy Duration: 27 Jul 2000 → 27 Jul 2000

Conference

Conference	International Joint Conference on Neural Networks
Abbreviated title	IJCNN 2000
Country/Territory	Italy
City	Como
Period	27/07/00 → 27/07/00

Keywords

Closed loop control systems
Computer simulation
Control system analysis
Control system synthesis
Identification (control systems)
Intelligent materials
Sensors
Transducers
Feedforward neural networks
Open loop control systems

Cite this

Developing smart micromachined transducers using feed-forward neural networks: A system identification and control perspective. / Gaura, E.I.; Rider, R.J.; Steele, N.
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000. IJCNN 2000. IEEE, 2000. p. 353-358.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

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title = "Developing smart micromachined transducers using feed-forward neural networks: A system identification and control perspective",

abstract = "The application of feedforward neural networks in micromachined acceleration sensors are discussed. Static and dynamic sensor identification is performed to improve the performance of neural, open- and closed-loop transducers. Simulation of the performance of the transducers indicated an extended measurement range compared to the `off-the-shelf' sensors.",

keywords = "Closed loop control systems, Computer simulation, Control system analysis, Control system synthesis, Identification (control systems), Intelligent materials, Sensors, Transducers, Feedforward neural networks, Open loop control systems",

author = "E.I. Gaura and R.J. Rider and N. Steele",

year = "2000",

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booktitle = "Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000. IJCNN 2000",

publisher = "IEEE",

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note = "International Joint Conference on Neural Networks : Neural Computing: New Challenges and Perspectives for the New Millennium , IJCNN 2000 ; Conference date: 27-07-2000 Through 27-07-2000",

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T2 - International Joint Conference on Neural Networks

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AU - Rider, R.J.

AU - Steele, N.

PY - 2000

Y1 - 2000

N2 - The application of feedforward neural networks in micromachined acceleration sensors are discussed. Static and dynamic sensor identification is performed to improve the performance of neural, open- and closed-loop transducers. Simulation of the performance of the transducers indicated an extended measurement range compared to the `off-the-shelf' sensors.

AB - The application of feedforward neural networks in micromachined acceleration sensors are discussed. Static and dynamic sensor identification is performed to improve the performance of neural, open- and closed-loop transducers. Simulation of the performance of the transducers indicated an extended measurement range compared to the `off-the-shelf' sensors.

KW - Closed loop control systems

KW - Computer simulation

KW - Control system analysis

KW - Control system synthesis

KW - Identification (control systems)

KW - Intelligent materials

KW - Sensors

KW - Transducers

KW - Feedforward neural networks

KW - Open loop control systems

UR - https://www.scopus.com/pages/publications/0033686672

M3 - Conference proceeding

SN - 0-7695-0619-4

SP - 353

EP - 358

BT - Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000. IJCNN 2000

PB - IEEE

Y2 - 27 July 2000 through 27 July 2000

ER -

Developing smart micromachined transducers using feed-forward neural networks: A system identification and control perspective

Abstract

Conference

Keywords

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