Hepatitis C dynamics' estimation process by differential neural networks

F. Miranda; N. Aguilar; A. Cabrera; I. Chairez

doi:10.1109/ijcnn.2006.247286

Hepatitis C dynamics' estimation process by differential neural networks

F. Miranda, N. Aguilar, A. Cabrera, I. Chairez

Unidad Profesional Interdisciplinaria de Biotecnología (UPIBI)

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

2 Citas (Scopus)

Resumen

Hepatitis C is one of the illness that have af fected many people around the world. It seriously harms the patient health in many ways. This paper provides a description of an adaptive nonlinear observer based on differential neural networks (DNN), designed for hepatitis C mathematical model, where all state vector is considered not to be available. Only viral load is assumed to be measurable with any analytical method like Reverse Transcriptase - Polymerase Chain Reaction (RT-PCR). The process is taken in two stages: a training scheme which generates the correct parameter set for the DNN-observer and the estimation process for three different inputs, which confirms (in numerical way) the robustness to input variations of the DNN scheme.

Idioma original	Inglés
Título de la publicación alojada	International Joint Conference on Neural Networks 2006, IJCNN '06
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	5301-5307
Número de páginas	7
ISBN (versión impresa)	0780394909, 9780780394902
DOI	https://doi.org/10.1109/ijcnn.2006.247286
Estado	Publicada - 2006
Evento	International Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canadá Duración: 16 jul. 2006 → 21 jul. 2006

Serie de la publicación

Nombre	IEEE International Conference on Neural Networks - Conference Proceedings
ISSN (versión impresa)	1098-7576

Conferencia

Conferencia	International Joint Conference on Neural Networks 2006, IJCNN '06
País/Territorio	Canadá
Ciudad	Vancouver, BC
Período	16/07/06 → 21/07/06

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

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10.1109/ijcnn.2006.247286

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Miranda, F., Aguilar, N., Cabrera, A., & Chairez, I. (2006). Hepatitis C dynamics' estimation process by differential neural networks. En International Joint Conference on Neural Networks 2006, IJCNN '06 (pp. 5301-5307). Artículo 1716837 (IEEE International Conference on Neural Networks - Conference Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ijcnn.2006.247286

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title = "Hepatitis C dynamics' estimation process by differential neural networks",

abstract = "Hepatitis C is one of the illness that have af fected many people around the world. It seriously harms the patient health in many ways. This paper provides a description of an adaptive nonlinear observer based on differential neural networks (DNN), designed for hepatitis C mathematical model, where all state vector is considered not to be available. Only viral load is assumed to be measurable with any analytical method like Reverse Transcriptase - Polymerase Chain Reaction (RT-PCR). The process is taken in two stages: a training scheme which generates the correct parameter set for the DNN-observer and the estimation process for three different inputs, which confirms (in numerical way) the robustness to input variations of the DNN scheme.",

keywords = "Differential neural networks, Hepatitis C, State estimation, System identification",

author = "F. Miranda and N. Aguilar and A. Cabrera and I. Chairez",

year = "2006",

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isbn = "0780394909",

series = "IEEE International Conference on Neural Networks - Conference Proceedings",

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pages = "5301--5307",

booktitle = "International Joint Conference on Neural Networks 2006, IJCNN '06",

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note = "International Joint Conference on Neural Networks 2006, IJCNN '06 ; Conference date: 16-07-2006 Through 21-07-2006",

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Miranda, F, Aguilar, N, Cabrera, A & Chairez, I 2006, Hepatitis C dynamics' estimation process by differential neural networks. En International Joint Conference on Neural Networks 2006, IJCNN '06., 1716837, IEEE International Conference on Neural Networks - Conference Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 5301-5307, International Joint Conference on Neural Networks 2006, IJCNN '06, Vancouver, BC, Canadá, 16/07/06. https://doi.org/10.1109/ijcnn.2006.247286

Hepatitis C dynamics' estimation process by differential neural networks. / Miranda, F.; Aguilar, N.; Cabrera, A. et al.
International Joint Conference on Neural Networks 2006, IJCNN '06. Institute of Electrical and Electronics Engineers Inc., 2006. p. 5301-5307 1716837 (IEEE International Conference on Neural Networks - Conference Proceedings).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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T1 - Hepatitis C dynamics' estimation process by differential neural networks

AU - Miranda, F.

AU - Aguilar, N.

AU - Cabrera, A.

AU - Chairez, I.

PY - 2006

Y1 - 2006

N2 - Hepatitis C is one of the illness that have af fected many people around the world. It seriously harms the patient health in many ways. This paper provides a description of an adaptive nonlinear observer based on differential neural networks (DNN), designed for hepatitis C mathematical model, where all state vector is considered not to be available. Only viral load is assumed to be measurable with any analytical method like Reverse Transcriptase - Polymerase Chain Reaction (RT-PCR). The process is taken in two stages: a training scheme which generates the correct parameter set for the DNN-observer and the estimation process for three different inputs, which confirms (in numerical way) the robustness to input variations of the DNN scheme.

AB - Hepatitis C is one of the illness that have af fected many people around the world. It seriously harms the patient health in many ways. This paper provides a description of an adaptive nonlinear observer based on differential neural networks (DNN), designed for hepatitis C mathematical model, where all state vector is considered not to be available. Only viral load is assumed to be measurable with any analytical method like Reverse Transcriptase - Polymerase Chain Reaction (RT-PCR). The process is taken in two stages: a training scheme which generates the correct parameter set for the DNN-observer and the estimation process for three different inputs, which confirms (in numerical way) the robustness to input variations of the DNN scheme.

KW - Differential neural networks

KW - Hepatitis C

KW - State estimation

KW - System identification

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M3 - Contribución a la conferencia

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SN - 0780394909

SN - 9780780394902

T3 - IEEE International Conference on Neural Networks - Conference Proceedings

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BT - International Joint Conference on Neural Networks 2006, IJCNN '06

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T2 - International Joint Conference on Neural Networks 2006, IJCNN '06

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Miranda F, Aguilar N, Cabrera A , Chairez I. Hepatitis C dynamics' estimation process by differential neural networks. En International Joint Conference on Neural Networks 2006, IJCNN '06. Institute of Electrical and Electronics Engineers Inc. 2006. p. 5301-5307. 1716837. (IEEE International Conference on Neural Networks - Conference Proceedings). doi: 10.1109/ijcnn.2006.247286

Hepatitis C dynamics' estimation process by differential neural networks

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