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Dynamic Calibration of Nonlinear Sensors with Time-Drifts and Delays by Bayesian Inference
Most sensor calibrations rely on the linearity and steadiness of their response characteristics, but practical sensors are nonlinear, and their response drifts with time, restricting their choices for adoption.
Robust Stability of Neural Network-controlled Nonlinear Systems with Parametric Variability
To develop a general theory for stability and stabilizability of a neural network (NN)-controlled nonlinear system subject to bounded parametric variation, a Lyapunov-based stability certificate is proposed and is further used to devise a maximal Lipschitz bound for the NN controller, and also a corresponding maximal region-of-attraction (RoA) inside a given safe operating domain.
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