Homotopy method for training neural networks

Lendl, Markus; Unbehauen, R.; Luo, Fang

doi:10.1016/s0920-5489(99)91033-4

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The Ppm-system Provides An Input For the Infusion Rate U[n]1

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1999

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“…The structure of the control unit makes it possible to implement a sufficient good approximation to the dynamic behaviour of the PPM-system by performing an iterative parameter adaption, the so-called training (e. g. [LUL98]), based on a recorded set of samples of u and T I . The trained predictor is then used by the Dynamic Matrix Control algorithm [CR80] for finding an appropriate value u[n + 11.…”

Section: The Ppm-system Provides An Input For the Infusion Rate U[n]mentioning

confidence: 99%

Closed-loop drug infusion using neural networks

Lendl

Geldner²,

Unbehauen³

1999

Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251)

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Closed-loop systems have been proofed as adequate for automated drug administration. They provide significant advantages for the patient as well as for the anesthesiologist.In essence three different types of implementing the control unit have been established: The classical linear controller (e. g. PID, adaptive PID), rule-based systems (e. g. fuzzy logic) and model-based controller (e. g. PWPD models). Major drawbacks of these implementations can be found in the limited flexibility or the expendable procedures for determining appropriate rules andlor model parameters.Our novel approach, applying neural networks in a predictive model-based design, is able to overcome the depicted difficulties. The performance has been verified by clinical investigations using the short acting non-depolarizing muscle relaxants mivacurium. The obtained results encourage further research using hypnotic drugs.

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Section: The Ppm-system Provides An Input For the Infusion Rate U[n]mentioning

confidence: 99%