Direct inverse control of sensors by neural networks for static/low frequency applications

N. C. Steele, Elena Gaura, R. J. Rider

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


This paper addresses the issue of direct inverse control for two types of nonlinear transducer systems characterised by:

piecewise linear input-output transfer function;

hysteresis occurring in the input-output transfer function;

with the aim of establishing whether some relationship exists between the severity of different nonlinearities and the complexity of the network required to control such nonlinearities in static/low-frequency sensor applications.

The compensation is performed using an artificial neural networks approach. The networks chosen were a static MLP and if tap-delayed line MLP, both trained by an improved BKP method which included a form of dynamic learning management.

Original languageEnglish
Title of host publicationArtificial Neural Nets and Genetic Algorithms
Subtitle of host publicationProceedings of the International Conference in Portorož, Slovenia, 1999
EditorsAndrej Dobnikar, Nigel C. Steele, David W. Pearson, Rudolf F. Albrecht
PublisherSpringer, Springer Nature
Number of pages6
ISBN (Electronic)9783709163849
ISBN (Print)3211833641, 9783211833643
Publication statusPublished - 1999
Externally publishedYes
EventInternational Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA 99) - PORTOROZ, Slovenia
Duration: 6 Apr 19999 Apr 1999


ConferenceInternational Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA 99)

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