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EKF learning for feedforward neural networks
2003 European Control Conference (ECC), 2003Learning for feedforward neural networks can be regarded as a nonlinear parameter estimation problem with the objective of finding the optimal weights that provide the best fitting of a given training set. The extended Kalman filter is well-suited to accomplishing this task, as it is a recursive state estimation method for nonlinear systems.
ALESSANDRI, ANGELO +4 more
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MCC-EKF for Autonomous Car Security
2020 Fourth IEEE International Conference on Robotic Computing (IRC), 2020This work attempts to answer two problems. (1) Can we use the odometry information from two different Simultaneous Localization And Mapping (SLAM) algorithms to get a better estimate of the odometry? and (2) What if one of the SLAM algorithms gets affected by shot noise or by attack vectors, and can we resolve this situation?
Ashutosh Singandhupe, Hung Manh La
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Head attitude tracking based on EKF
SPIE Proceedings, 2012As there exists a high mean-square error for the monocular vision PNP algorithms in solving the problem of continuous head attitude tracking especially at certain relative orientations, this paper presented a new refinement method based on Extended Kalman Filter.
Kai Ma +3 more
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MIMO Propagation Parameter Tracking using EKF
2006 IEEE Nonlinear Statistical Signal Processing Workshop, 2006In this paper we describe the application of extracting the MIMO radio channel propagation parameters from channel sounding measurements using the Extended Kalman Filter. This approach allows to capture the dynamics of the radio propagation channels and enables recursive, computationally low-complexity (compared with traditional iterative maximum ...
Jussi Salmi +2 more
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IMPACT OF PERTURBATION ESTIMATOR ON EKF-SLAM RESULTS
IFAC Proceedings Volumes, 2007Abstract This paper deals with the Simultaneous Localization and Map building (SLAM) problem using an Extended Kalman Filter with Perturbation Estimator (EKFPE). The perturbation estimator is used to take systematic errors into account. These disturbance estimates are added to vehicle states in a feedback loop to improve location and map estimates ...
Chanier, François +3 more
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Exact Learning Curves for EKF Training
1998We formulate a learning algorithm for online learning in neural networks using the principles of the Extended Kaiman Filter approach. This gives rise to a Bayesian learning scheme where the learning rate parameter is adapted automatically. The approach is applied to regression problems whereby the rule to be learned is non-stationary, examining both ...
B. Schottky, D. Saad
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Geomagnetic Surface Navigation Using Adaptive EKF
2007 2nd IEEE Conference on Industrial Electronics and Applications, 2007In need of accurate and covert positioning, a geomagnetic surface navigation system is investigated in the paper. The Earth anomaly magnetic field intensity is selected as the reference to bound the INS (inertial navigation system) errors. Using stochastic linearization technique, we have proposed an EKF (extended Kalman filtering) algorithm which is ...
Hua Mu +3 more
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Consistency of SLAM-EKF Algorithms for Indoor Environments
Journal of Intelligent and Robotic Systems, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rodriguez-Losada, Diego +4 more
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A complex EKF-RTRL neural network
IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222), 2002The purpose of the paper is to use extended Kalman filter (EKF) techniques in the complex real time recurrent learning (RTRL) neural network in order to have faster convergence as an alternative to standard gradient methods, usually used in RTRL neural networks training which are known to be slow.
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