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A Parallel Estimation Algorithm

  • M. Hodzic
  • D. D. Siljak
Part of the Applied Information Technology book series (AITE)

Abstract

A parallel estimation algorithm is proposed for large scale dynamic systems. The algorithm is bi-modal in that the local estimates are computed in parallel at high sampling rates considering the subsystems as decoupled from each other. At the same time, the outputs of the local estimators are used as initial data for the overall estimator which takes the interaction effects into account in a block-iterative fashion. Each individual locally optimal estimator can have a different sampling rate to match the subsystem dynamics.

Keywords

Gain Matrix Optimal Estimator High Sampling Rate Individual Subsystem Subsystem Dynamic 
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.

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References

  1. B.D.O. Anderson and J.B. Moore (1979). Optimal Filtering, Prentice Hall, Englewood Cliffs, New Jersey.MATHGoogle Scholar
  2. M. Hodzic and H. AlKhatib (1983). Distributed architecture for implementation of an algorithm for estimation of sparse large scale dynamic systems, ISMM Conference MIMI’83, San Antonio, Texas.Google Scholar
  3. M. Hodzic and D.D. Siljak (1985). Estimation and control of large sparse systems, Automatica, 21, pp. 277–292.MATHCrossRefGoogle Scholar
  4. Pichai, V., M.E. Sezer and D.D. Siljak (1983). A graph-theoretic Algorithm for hierarchical decomposition of dynamic systems with application to estimation and control, IEEE Transactions, SMC-13, pp. 197–207.MathSciNetGoogle Scholar
  5. Siljak, D.D. (1978). Large-Scale Dynamic Systems: Stability and Structure, North Holland, New York.MATHGoogle Scholar

Copyright information

© Plenum Press, New York 1986

Authors and Affiliations

  • M. Hodzic
    • 1
  • D. D. Siljak
    • 1
  1. 1.School of Engineering, EECS DepartmentSanta Clara UniversitySanta ClaraUSA

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