Hector M. Romero Ugalde

Orcid: 0000-0002-7614-6474

According to our database1, Hector M. Romero Ugalde authored at least 12 papers between 2013 and 2022.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2022
Fractal-fractional neuro-adaptive method for system identification.
Eng. Comput., 2022

Numerical solution of fractal-fractional Mittag-Leffler differential equations with variable-order using artificial neural networks.
Eng. Comput., 2022

2019
Modeling the variability of insulin sensitivity for people with Type 1 Diabetes based on clinical data from an artificial pancreas study.
Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2019

2018
Closed-Loop Vagus Nerve Stimulation Based on State Transition Models.
IEEE Trans. Biomed. Eng., 2018

2017
Acute effect of Vagus nerve stimulation parameters on cardiac chronotropic, inotropic, and dromotropic responses.
Proceedings of the 13th International Symposium on Medical Information Processing and Analysis, 2017

2016
Model-Based Design and Experimental Validation of Control Modules for Neuromodulation Devices.
IEEE Trans. Biomed. Eng., 2016

2015
Huberian approach for reduced order ARMA modeling of neurodegenerative disorder signal.
Signal Process., 2015

Balanced simplicity-accuracy neural network model families for system identification.
Neural Comput. Appl., 2015

Computational cost improvement of neural network models in black box nonlinear system identification.
Neurocomputing, 2015

Model-based design of control modules for neuromodulation devices.
Proceedings of the 7th International IEEE/EMBS Conference on Neural Engineering, 2015

2014
On-off closed-loop control of vagus nerve stimulation for the adaptation of heart rate.
Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014

2013
Neural network design and model reduction approach for black box nonlinear system identification with reduced number of parameters.
Neurocomputing, 2013


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