On the convergence of EKF-based parameters optimization for neural ...
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Abstract: An algorithm based on the extended Kalman filter (EKF) for optimization of parameters in neural networks is presented and a convergence analysis ...
Abstract: An algorithm based on the extended Kalman filter (EKF) for optimization of parameters in neural networks is presented and a convergence analysis ...
PDF | An algorithm based on the extended Kalman filter (EKF) for optimization of parameters in neural networks is presented and a convergence analysis.
The ALRs of the EFK based training algorithm produce the convergence of the WNN. Also we derive the convergence analysis of the learning process from the ...
The results show that the R adaption law can effectively avoid the divergence problem and ensure the training convergence, whereas the Q ada adaptation law ...
Learning for feedforward neural networks can be regarded as a nonlinear parameter estimation problem with the objective of finding the optimal weights that ...
... To the best of our knowledge, our paper is the first study that provides a theoretical convergence guarantee for an IEKF-based algorithm in the neural ...
Oct 22, 2019 · This work introduces a highly efficient extended Kalman filter (EKF) based training algorithm with a theoretical convergence guarantee for ...
On the convergence EKF-based parameters optimization for neural networks. Alessandri A;Cuneo M;Pagnan S;Sanguineti M. 2003-01-01. Scheda breve; Scheda completa ...
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As a result this paper concentrates on the parameter estimation and presents the Kalman filter as an appropriate optimisation method. First, the basic ...
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