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

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

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

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 languageEnglish
Title of host publicationProceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000. IJCNN 2000
PublisherIEEE
Pages353-358
Number of pages6
ISBN (Print)0-7695-0619-4
Publication statusPublished - 2000
EventInternational Joint Conference on Neural Networks: Neural Computing: New Challenges and Perspectives for the New Millennium - Como, Italy
Duration: 27 Jul 200027 Jul 2000

Conference

ConferenceInternational Joint Conference on Neural Networks
Abbreviated titleIJCNN 2000
CountryItaly
CityComo
Period27/07/0027/07/00

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

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