Abstract
SummaryEffectiveness of data‐driven neural learning in terms of both local mimima trapping and convergence rate is addressed. Such issues are investigated in a case study involving the training of one‐hidden‐layer feedforward neural networks with the extended Kalman filter, which reduces the search for the optimal network parameters to a state estimation problem, as compared to descent‐based methods. In this respect, the performances of the training are assessed by using the Cramér‐Rao bound, along with a novel metric based on an empirical criterion to evaluate robustness with respect to local minima trapping. Numerical results are provided to illustrate the performances of the training based on the extended Kalman filter in comparison with gradient‐based learning.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Adaptive Control and Signal Processing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
