  This paper focuses on the identification of dynamical systems with
tailor-made model structures, where neural networks are used to approximate
uncertain components and domain knowledge is retained, if available. These
model structures are fitted to measured data using different criteria including
a computationally efficient approach minimizing a regularized multi-step ahead
simulation error. In this approach, the neural network parameters are estimated
along with the initial conditions used to simulate the output signal in
small-size subsequences. A regularization term is included in the fitting cost
in order to enforce these initial conditions to be consistent with the
estimated system dynamics. Pitfalls and limitations of naive one-step
prediction and simulation error minimization are also discussed.
