Abstract
Self-learning fuzzy logic control has the important property of accommodating uncertain, non-linear and time-varying process characteristics. This intelligent control scheme starts with no fuzzy control rules and learns how to control each process presented to it in real-time without the need for detailed process modelling. Medicine abounds with suitable applications for this technique. Following an outline of the methodology we demonstrate its clinical effectiveness for application in anaesthesia. We have investigated its application to atracurium-induced neuromuscular block during surgery and have observed improved control over complex numerical techniques. This self-learning fuzzy control technique shows much promise for other medical applications such as post-operative blood pressure management, intra-operative control of anaesthetic depth, and multivariable circulatory management of intensive care patients.
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© 1997 Springer-Verlag Berlin Heidelberg
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Mason, D.G., Linkens, D.A., Edwards, N.D. (1997). Self-learning fuzzy logic control in medicine. In: Keravnou, E., Garbay, C., Baud, R., Wyatt, J. (eds) Artificial Intelligence in Medicine. AIME 1997. Lecture Notes in Computer Science, vol 1211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0029463
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DOI: https://doi.org/10.1007/BFb0029463
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Print ISBN: 978-3-540-62709-8
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