Abstract
This paper addresses the issue of direct inverse control for two types of nonlinear transducer systems characterised by:
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piecewise linear input-output transfer function;
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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 a tap-delayed line MLP, both trained by an improved BKP method which included a form of dynamic learning management.
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© 1999 Springer-Verlag Wien
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Steele, N., Gaura, E., Rider, R.J. (1999). Direct Inverse Control of Sensors by Neural Networks for Static/Low Frequency Applications. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6384-9_24
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DOI: https://doi.org/10.1007/978-3-7091-6384-9_24
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83364-3
Online ISBN: 978-3-7091-6384-9
eBook Packages: Springer Book Archive