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This paper focuses on the identification of dynamical systems withtailor-made model structures, where neural networks are used to approximateuncertain components and domain knowledge is retained, if available. Thesemodel structures are fitted to measured data using different criteria includinga computationally efficient approach minimizing a regularized multi-step aheadsimulation error. In this approach, the neural network parameters are estimatedalong with the initial conditions used to simulate the output signal insmall-size subsequences. A regularization term is included in the fitting costin order to enforce these initial conditions to be consistent with theestimated system dynamics. Pitfalls and limitations of naive one-stepprediction and simulation error minimization are also discussed.

Model structures and fitting criteria for system identification with neural networks