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- W1974799915 abstract "Abstract The article presents a novel adaptive iterative calibration algorithm, which can be successfully applied for the calibration constants of a 3-axis accelerometer determination. The theory is based on the neural network that creates an inverse function to the uncalibrated sensor's transfer function. Learning process of the neural network uses a gradient methodology applying total differential on the scalar error equation. The performed simulations and experiments proved a very good stability and fast convergence of the iterative algorithm and a major improvement of the sensor's output accuracy. In addition to the easy-to-use calibration algorithm, considerably fast convergence, precision, noticeable repeatability and undemandingness of the measurements, the main advantage of this methodology lies in the sensor attitude independency, which is a requirement and limitation of many other calibration methods and no need of the precision positioning calibration platforms, which leads to the calibration errors reduction." @default.
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- W1974799915 date "2014-01-01" @default.
- W1974799915 modified "2023-09-27" @default.
- W1974799915 title "Attitude-independent 3-axis Accelerometer Calibration Based on Adaptive Neural network" @default.
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- W1974799915 doi "https://doi.org/10.1016/j.proeng.2014.11.412" @default.
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