IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
Kalman Filtering and Neural Networks
Kalman Filtering and Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Network Control of Nonlinear Discrete-Time Systems (Public Administration and Public Policy)
Neural Network Control of Nonlinear Discrete-Time Systems (Public Administration and Public Policy)
Monte Carlo Strategies in Scientific Computing
Monte Carlo Strategies in Scientific Computing
Nonlinear adaptive prediction of nonstationary signals
IEEE Transactions on Signal Processing
New developments in state estimation for nonlinear systems
Automatica (Journal of IFAC)
On the Kalman filtering method in neural network training and pruning
IEEE Transactions on Neural Networks
Training Recurrent Neurocontrollers for Robustness With Derivative-Free Kalman Filter
IEEE Transactions on Neural Networks
An algorithm for on-line detection of high frequency oscillations related to epilepsy
Computer Methods and Programs in Biomedicine
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In this paper, we present nonlinear Bayesian filters for training recurrent neural networks with a special emphasis on a novel, more accurate, derivative-free member of the approximate Bayesian filter family called the cubature Kalman filter. We discuss the theory of Bayesian filters, which is rooted in the state-space modeling of the dynamic system in question and the linear estimation principle. For improved numerical stability and optimal performance during training period, a number of techniques of how to tune Bayesian filters is suggested. We compare the predictability of various Bayesian filter-trained recurrent neural networks using a chaotic time-series. From the empirical results, we conclude that the performance may be greatly improved by the new square-root cubature Kalman filter.