Skip to main content
Log in

Differential geometry of a parametric family of invertible linear systems—Riemannian metric, dual affine connections, and divergence

  • Published:
Mathematical systems theory Aims and scope Submit manuscript

Abstract

A parametric model of systems is regarded as a geometric manifold imbedded in the enveloping manifold consisting of all the linear systems. The present paper aims at establishing a new geometrical method and framework for analyzing properties of manifolds of systems. A Riemannian metric and a pair of dual affine connections are introduced to a system manifold. They are calledα-connections. The duality of connections is a new concept in differential geometry. The manifold of all the linear systems isα-flat so that it admits natural and invariantα-divergence measures. Such geometric structures are useful for treating the problems of approximation, identification, and stochastic realization of systems. By using theα-divergences, we solve the problem of approximating a given system by one included in a model. For a sequence ofα-flat nesting models such as AR models and MA models, it is shown that the approximation errors are decomposed additively corresponding to each dimension of the model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. S. Amari, Differential geometry of curved exponential families—curvatures and information loss,Ann. Statist.,10 (1982), 357–387.

    Google Scholar 

  2. S. Amari, A foundation of information geometry,Electron. Comm. Japan,66 (1983), 1–10.

    Google Scholar 

  3. S. Amari, Differential geometry of systems, Report No. 528, Institute of Mathematical Analysis, Kyoto University, 1984, pp. 235–253.

  4. S. Amari,Differential Geometrical Methods in Statistics, Lecture Notes in Statistics, vol. 28, Springer-Verlag, New York, 1985.

    Google Scholar 

  5. S. Amari, Differential geometry of statistics—towards new developments, inDifferential Geometry in Statistical Inference, IMS Monograph, Institute of Mathematical Statistics, Hayward, CA, 1987.

    Google Scholar 

  6. R. J. Bhansali, The inverse partial correlation function of a time series and its application,J. Multivariate Anal.,13 (1983), 310–327.

    Google Scholar 

  7. P. Bloomfield, An exponential model for the spectrum of a scalar time series,Biometrika,60 (1973), 217–226.

    Google Scholar 

  8. R. W. Brockett, Some geometric questions in the theory of linear systems,IEEE Trans. Automat. Control,21 (1976), 449–455.

    Google Scholar 

  9. R. W. Brockett and P. S. Krishnaprasad, A scaling theory for linear systems,IEEE Trans. Automat. Control,25 (1980), 197–207.

    Google Scholar 

  10. J. P. Burg, Maximum entropy spectral analysis,Proceedings of the 37th Meeting of the Society of Exploration Geophysicists, 1967. Also inModern Spectrum Analysis (D. G. Childers, ed.), IEEE Press, New York, 1978.

  11. C. I. Byrnes, Algebraic and geometric aspects of the analysis of feedback systems, inGeometrical Methods for the Theory of Linear Systems (C. I. Byrnes and C. F. Martin, eds), Reidel, Boston, 1980, pp. 85–124.

    Google Scholar 

  12. N. N. Chentsov,Statistical Decision and Optimal Inference, Nauka, Moscow, 1972 (in Russian); translation in English, American Mathematical Society, Providence, RI, 1982.

    Google Scholar 

  13. D. F. Delchamps, Global structure of families of multivariable linear systems with an application to identification,Math. Systems Theory,18 (1985) 329–380.

    Google Scholar 

  14. P. L. Duren,Theory of H p Spaces, Academic Press, New York, 1970.

    Google Scholar 

  15. E. J. Hannan, Estimating the dimension of a linear system,J. Multivariate Anal.,11 (1981), 459–473.

    Google Scholar 

  16. E. J. Hannan and M. Deistler, Some properties of the parametrization of ARMA systems with unknown order,J. Multivariate Anal.,11 (1981), 474–484.

    Google Scholar 

  17. M. Hazewinkel, Moduli and canonical forms for dynamical systems, II: the topological case,Math. Systems Theory,10 (1977), 363–385.

    Google Scholar 

  18. M. Hazewinkel, (Fine) Moduli (spaces) for linear systems: what are they and what are they good for?, inGeometrical Methods for the Theory of Linear Systems (C. I. Byrens and C. F. Martin, eds), Reidel, Boston, 1980, pp. 125–193.

    Google Scholar 

  19. R. Hermann and C. F. Martin, Applications of algebraic geometry to systems theory: the McMillan degree and Kronecker indices of transfer functions as topological and holomorphic system invariants,SIAM J. Control Optim.,16 (1978), 743–755.

    Google Scholar 

  20. R. W. Johnson and E. Shore, Minimum cross entropy spectral analysis of multiple signals,IEEE Trans. Acoust. Speech Signal Process.,31 (1983), 574–582.

    Google Scholar 

  21. R. E. Kalman, Mathematical description of linear dynamical systems,SIAM J. Control Optim.,1 (1963), 128–151.

    Google Scholar 

  22. W. Klingenberg,Riemmanian Geometry, Walter de Gruyter, Berlin, 1982.

    Google Scholar 

  23. M. Kumon and S. Amari, Geometrical theory of higher-order asymptotics of test, interval estimator and conditional inference,Proc. Roy. Soc. London Ser. A,387 (1983), 429–458.

    Google Scholar 

  24. M. Kumon and S. Amari, Estimation of a structural parameter in the presence of a large number of nuisance parameters,Biometrika,71 (1984), 445–459.

    Google Scholar 

  25. S. Lang,Differentiable Manifolds, Addison-Wesley, Reading, MA, 1972.

    Google Scholar 

  26. S. Lauritzen,Some Differential Geometrical Notions and Their Use in Statistical Theory, IMS Monograph, Institute of Mathematical Statistics, Hayward, CA, 1987.

    Google Scholar 

  27. H. Nagaoka and S. Amari, Differential geometry of smooth families of probability distributions, Technical Report METR 82-7, University of Tokyo, 1982.

  28. C. R. Rao,Linear Statistical Inference and Its Applications, Wiley, New York, 1965.

    Google Scholar 

  29. G. Segal, The topology of spaces of rational functions,Acta Math.,143 (1979), 39–72.

    Google Scholar 

  30. E. Shore, Minimum cross-entropy spectral analysis,IEEE Trans. Acoust. Speech Signal Process.,29 (1981), 230–237.

    Google Scholar 

  31. A. Tannenbaum,Invariance and System Theory: Algebraic and Geometric Aspects, Lecture Notes in Mathematics, vol. 845, Springer-Verlag, New York, 1981.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Amari, Si. Differential geometry of a parametric family of invertible linear systems—Riemannian metric, dual affine connections, and divergence. Math. Systems Theory 20, 53–82 (1987). https://doi.org/10.1007/BF01692059

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01692059

Keywords

Navigation