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- W2295640415 abstract "In the present work we designed a neuro-fuzzy approach for on-line optimal tuning of a Kalman filter of a gyroscope within a Micro ElectroMechanical Sensor (MEMS) device. It consists of Adaptive Critic Design (ACD) scheme in which the controller (a Fuzzy Rule Base (FRB) designed to adapt the measurement noise covariance matrix of a Kalman filter) is tuned using only information about the direction to which the estimation error changes (increase or decrease). A novel fast training dynamic neural network structure - Echo state network (ESN) - was used in the role of the critic element. Application to data collected from real MEMS demonstrated the ability of the proposed approach to tune Kalman filter and improve the quality of its estimates in changing working conditions of the MEMS in real time." @default.
- W2295640415 created "2016-06-24" @default.
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- W2295640415 date "2015-01-01" @default.
- W2295640415 modified "2023-10-14" @default.
- W2295640415 title "ACD with ESN for Tuning of MEMS Kalman Filter" @default.
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- W2295640415 doi "https://doi.org/10.1007/978-3-319-26520-9_24" @default.
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