Neural network training with the nprKF

Proceedings of the International Joint Conference on Neural Networks

Volumn 1, Issue , 2001, Pages 109-114

  Feldkamp, L A ^a   Feldkamp, T M ^a   Prokhorov, D V ^a  

^a FORD RESEARCH LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; FEEDFORWARD NEURAL NETWORKS; KALMAN FILTERING; MATHEMATICAL TRANSFORMATIONS; MATRIX ALGEBRA; NONLINEAR SYSTEMS; RECURRENT NEURAL NETWORKS; SPURIOUS SIGNAL NOISE; VECTORS;

EXTENDED KALMAN FILTER; NEURAL NETWORK TRAINING; SIGMA VECTORS; TAYLOR EXPANSION; UNSCENTED KALMAN FILTER;

LEARNING SYSTEMS;

EID: 0034856561     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

1. A signal processing framework based on dynamic neural networks with application to problems in adaptation, filtering and classification
2. The unscented kalman filter for nonlinear estimation
3. The unscented kalman filter
4. A new approach for filtering nonlinear systems
5. Advances in derivative-free state estimation for nonlinear systems
6. Roles of learning rates, artificial process noise and square root filtering for extended kalman filter training
7. Parameter-based kalman filter training: Theory and application
8. Training multilayer perceptrons with the extended kalman algorithm