Abstract
State-dependent parameter representations of nonlinear stochastic sampled-data systems are studied. Velocity-based linearization is used characterize sampled-data systems using nominally linear models whose parameters can be represented as functions of past outputs and inputs. For stochastic systems the approach leads to state-dependent ARMAX (quasi-ARMAX) representations. The models and their parameters are identified from input-output data using feedforward neural networks to represent the model parameters as functions of past inputs and outputs.
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