Kalman Filtering is machine learning. It has been used as a neural network training method.
I'm implementing real-time dual state/parameter estimators at the limits of computational tractability. Please tell me how "machine learning" can solve my problem better.
The state dynamics are linear but they are time varying and unknown. I am tracking the dynamics with a second Kalman filter (dual UKF like structure but mostly linear). Main "problem area" is covariance propagation, since the dynamics are defined by GRVs estimated by the parameter estimator but this uncertainty is not propagated between state/parameter estimators. It's a known shortcoming with standard (UKF) dual parameter+state estimation. I've yet to conclude whether it's a real or imaginary problem for my use-case.
I'm implementing real-time dual state/parameter estimators at the limits of computational tractability. Please tell me how "machine learning" can solve my problem better.