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This paper investigates the use of extended Kalman filtering to trainrecurrent neural networks with rather general convex loss functions andregularization terms on the network parameters, including$\ell_1$-regularization. We show that the learning method is competitive withrespect to stochastic gradient descent in a nonlinear system identificationbenchmark and in training a linear system with binary outputs. We also explorethe use of the algorithm in data-driven nonlinear model predictive control andits relation with disturbance models for offset-free closed-loop tracking.

Recurrent Neural Network Training with Convex Loss and Regularization Functions by Extended Kalman Filtering