This paper investigates the use of extended Kalman filtering to train
recurrent neural networks with rather general convex loss functions and
regularization terms on the network parameters, including
β1β-regularization. We show that the learning method is competitive with
respect to stochastic gradient descent in a nonlinear system identification
benchmark and in training a linear system with binary outputs. We also explore
the use of the algorithm in data-driven nonlinear model predictive control and
its relation with disturbance models for offset-free closed-loop tracking.Comment: 21 pages, 3 figures, submitted for publicatio