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State-Dependent Parameter Modelling and Identification of Stochastic Non-Linear Sampled-Data Systems

Bernt M. Åkesson, Hannu T. Toivonen, State-Dependent Parameter Modelling and Identification of Stochastic Non-Linear Sampled-Data Systems. Journal of Process Control 16(8), 877–886, 2006.

Abstract:

State-dependent parameter representations of stochastic non-linear sampled-data systems are studied. Velocity-based linearization is used to construct state-dependent parameter models which have a nominally linear structure but whose parameters can be characterized as functions of past outputs and inputs. For stochastic systems state-dependent parameter ARMAX (quasi-ARMAX) representations are obtained. The models are identified from input–output data using feedforward neural networks to represent the model parameters as functions of past inputs and outputs. Simulated examples are presented to illustrate the usefulness of the proposed approach for the modelling and identification of non-linear stochastic sampled-data systems.

BibTeX entry:

@ARTICLE{jAkTo06a,
  title = {State-Dependent Parameter Modelling and Identification of Stochastic Non-Linear Sampled-Data Systems},
  author = {Åkesson, Bernt M. and Toivonen, Hannu T.},
  journal = {Journal of Process Control},
  volume = {16},
  number = {8},
  publisher = {Elsevier},
  pages = {877–886},
  year = {2006},
  keywords = {Sampled-data systems; Neural network models; Stochastic systems },
}

Belongs to TUCS Research Unit(s): Other

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