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

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

doi:10.1007/978-3-7091-6384-9

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 proceeding › Conference proceeding contribution › peer-review

Abstract

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 language	English
Title of host publication	Artificial Neural Nets and Genetic Algorithms
Subtitle of host publication	Proceedings of the International Conference in Portorož, Slovenia, 1999
Editors	Andrej Dobnikar, Nigel C. Steele, David W. Pearson, Rudolf F. Albrecht
Publisher	Springer, Springer Nature
Pages	135-140
Number of pages	6
Edition	1
ISBN (Electronic)	9783709163849
ISBN (Print)	3211833641, 9783211833643
DOIs	https://doi.org/10.1007/978-3-7091-6384-9
Publication status	Published - 1999
Externally published	Yes
Event	International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA 99) - PORTOROZ, Slovenia Duration: 6 Apr 1999 → 9 Apr 1999

Conference

Conference	International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA 99)
Country/Territory	Slovenia
City	PORTOROZ
Period	6/04/99 → 9/04/99

Access to Document

10.1007/978-3-7091-6384-9

Cite this

Steele, N. C., Gaura, E., & Rider, R. J. (1999). Direct inverse control of sensors by neural networks for static/low frequency applications. In A. Dobnikar, N. C. Steele, D. W. Pearson, & R. F. Albrecht (Eds.), Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference in Portorož, Slovenia, 1999 (1 ed., pp. 135-140). Springer, Springer Nature. https://doi.org/10.1007/978-3-7091-6384-9

Steele, N. C. ; Gaura, Elena ; Rider, R. J. / Direct inverse control of sensors by neural networks for static/low frequency applications. Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference in Portorož, Slovenia, 1999. editor / Andrej Dobnikar ; Nigel C. Steele ; David W. Pearson ; Rudolf F. Albrecht. 1. ed. Springer, Springer Nature, 1999. pp. 135-140

@inproceedings{888bfbf2a6284c669ea82c27f1edda19,

title = "Direct inverse control of sensors by neural networks for static/low frequency applications",

abstract = "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.",

author = "Steele, {N. C.} and Elena Gaura and Rider, {R. J.}",

year = "1999",

doi = "10.1007/978-3-7091-6384-9",

language = "English",

isbn = "3211833641",

pages = "135--140",

editor = "Andrej Dobnikar and Steele, {Nigel C.} and Pearson, {David W.} and Albrecht, {Rudolf F.}",

booktitle = "Artificial Neural Nets and Genetic Algorithms",

publisher = "Springer, Springer Nature",

address = "United States",

edition = "1",

note = "International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA 99) ; Conference date: 06-04-1999 Through 09-04-1999",

}

Steele, NC, Gaura, E & Rider, RJ 1999, Direct inverse control of sensors by neural networks for static/low frequency applications. in A Dobnikar, NC Steele, DW Pearson & RF Albrecht (eds), Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference in Portorož, Slovenia, 1999. 1 edn, Springer, Springer Nature, pp. 135-140, International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA 99), PORTOROZ, Slovenia, 6/04/99. https://doi.org/10.1007/978-3-7091-6384-9

Direct inverse control of sensors by neural networks for static/low frequency applications. / Steele, N. C.; Gaura, Elena; Rider, R. J.
Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference in Portorož, Slovenia, 1999. ed. / Andrej Dobnikar; Nigel C. Steele; David W. Pearson; Rudolf F. Albrecht. 1. ed. Springer, Springer Nature, 1999. p. 135-140.

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

TY - GEN

T1 - Direct inverse control of sensors by neural networks for static/low frequency applications

AU - Steele, N. C.

AU - Gaura, Elena

AU - Rider, R. J.

PY - 1999

Y1 - 1999

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-7091-6384-9

DO - 10.1007/978-3-7091-6384-9

M3 - Conference proceeding contribution

SN - 3211833641

SN - 9783211833643

SP - 135

EP - 140

BT - Artificial Neural Nets and Genetic Algorithms

A2 - Dobnikar, Andrej

A2 - Steele, Nigel C.

A2 - Pearson, David W.

A2 - Albrecht, Rudolf F.

PB - Springer, Springer Nature

T2 - International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA 99)

Y2 - 6 April 1999 through 9 April 1999

ER -

Steele NC, Gaura E, Rider RJ. Direct inverse control of sensors by neural networks for static/low frequency applications. In Dobnikar A, Steele NC, Pearson DW, Albrecht RF, editors, Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference in Portorož, Slovenia, 1999. 1 ed. Springer, Springer Nature. 1999. p. 135-140 doi: 10.1007/978-3-7091-6384-9

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

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