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. © 2006 IEEE.
|Original language||American English|
|Number of pages||4770|
|State||Published - 1 Dec 2006|
|Event||IEEE International Conference on Neural Networks - Conference Proceedings - |
Duration: 1 Dec 2007 → …
|Conference||IEEE International Conference on Neural Networks - Conference Proceedings|
|Period||1/12/07 → …|
Miranda, F., Aguilar, N., Cabrera, A., & Chairez, I. (2006). Hepatitis C dynamics' estimation process by differential neural networks. 5301-5307. Paper presented at IEEE International Conference on Neural Networks - Conference Proceedings, .