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Model-Based Design and Experimental Validation of Control Modules for Neuromodulation Devices.

, , , , , , , , , , , and . IEEE Trans. Biomed. Eng., 63 (7): 1551-1558 (2016)

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Computational cost improvement of neural network models in black box nonlinear system identification., , , , and . Neurocomputing, (2015)Balanced simplicity-accuracy neural network model families for system identification., , , , and . Neural Comput. Appl., 26 (1): 171-186 (2015)Model-based design of control modules for neuromodulation devices., , , , , , , , , and . NER, page 462-465. IEEE, (2015)Model-Based Design and Experimental Validation of Control Modules for Neuromodulation Devices., , , , , , , , , and 2 other author(s). IEEE Trans. Biomed. Eng., 63 (7): 1551-1558 (2016)Acute effect of Vagus nerve stimulation parameters on cardiac chronotropic, inotropic, and dromotropic responses., , , , , , , , , and . SIPAIM, volume 10572 of SPIE Proceedings, page 105721C. SPIE, (2017)On-off closed-loop control of vagus nerve stimulation for the adaptation of heart rate., , , , , , , and . EMBC, page 6262-6265. IEEE, (2014)Numerical solution of fractal-fractional Mittag-Leffler differential equations with variable-order using artificial neural networks., , , , , and . Eng. Comput., 38 (3): 2669-2682 (2022)Neural network design and model reduction approach for black box nonlinear system identification with reduced number of parameters., , , and . Neurocomputing, (2013)Closed-Loop Vagus Nerve Stimulation Based on State Transition Models., , , , , , and . IEEE Trans. Biomed. Eng., 65 (7): 1630-1638 (2018)