Open Access
A novel algorithm for the modelling of complex processes
José de Jesús Rubio,Edwin Lughofer,Plamen Angelov,Juan Francisco Novoa,Jesus Alberto Meda-Campana +4 more
- 01 Apr 2018
1
TL;DR: In this investigation, a new algorithm is developed for the updating of a neural network in a fuzzy transition between the recursive least square and extended Kalman filter algorithms with the purpose to get a bounded gain such that a satisfactory modeling could be maintained.
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Abstract: In this investigation, a new algorithm is developed for the updating of a neural network. It is concentrated in a fuzzy transition between the recursive least square and extended Kalman filter algorithms with the purpose to get a bounded gain such that a satisfactory modeling could be maintained. The advised algorithm has the advantage compared with the mentioned methods that it eludes the excessive increasing or decreasing of its gain. The gain of the recommended algorithm is uniformly stable and its convergence is found. The new algorithm is employed for the modeling of two synthetic examples.
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Citations
DTM-Aided Adaptive EPF Navigation Application in Railways.
TL;DR: A feasible Digital Track Map- aided (DTM-aided) adaptive extended Kalman particle filter method is introduced in GNSS/INS integration in order to adjust GNSS measurement noise covariance in real time, and the GNSS down-direction offset is estimated along with every sampling period through making full use of DTM information.
References
DTM-Aided Adaptive EPF Navigation Application in Railways.
TL;DR: A feasible Digital Track Map- aided (DTM-aided) adaptive extended Kalman particle filter method is introduced in GNSS/INS integration in order to adjust GNSS measurement noise covariance in real time, and the GNSS down-direction offset is estimated along with every sampling period through making full use of DTM information.