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Approximate Sampled-Data Models for Linear Stochastic Systems

  • Juan I. Yuz
  • Graham C. Goodwin
Part of the Communications and Control Engineering book series (CCE)

Abstract

This chapter shows how approximate sampled-data models for stochastic-linear systems can be developed. In the linear case exact sampled-data models can always be obtained. However, results on approximate linear-stochastic sampled-data models are developed here, as a prelude to the nonlinear case treated in the next chapter.

Keywords

Power Spectral Density Noise Process Approximate Model Stochastic Case Linear Stochastic System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Further Reading

For a discussion on innovations representations, see

  1. Anderson BDO, Moore JB (1979) Optimal filtering. Dover, New York Google Scholar

Up-sampling for stochastic sampled-data models has been discussed in

  1. Cea MG, Goodwin GC, Mueller C (2011) A novel technique based on up-sampling for addressing modeling issues in sampled data nonlinear filtering. In: 18th IFAC world congress, Milan, Italy Google Scholar

Sampled-data models based on stochastic integration were first discussed in

  1. Yuz JI, Goodwin GC (2006) Sampled-data models for stochastic nonlinear systems. In: 14th IFAC symposium on system identification, Newcastle, Australia Google Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Juan I. Yuz
    • 1
  • Graham C. Goodwin
    • 2
  1. 1.Departamento de ElectrónicaUniversidad Técnica Federico Santa MaríaValparaísoChile
  2. 2.School of Electrical Engineering & Computer ScienceUniversity of NewcastleCallaghanAustralia

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