Digital Signal Processing Reference
In-Depth Information
33. J. C. Fort and G. Pag
`
s, Convergence of stochastic algorithms: from the Kushner and
Clark theorem to the Lyapunov functional method,
Advances in Applied Probability
,
No. 28, pp. 1072-1094, (1996).
34. B. Friedlander and A. J. Weiss, On the second-order of the eigenvectors of sample
covariance matrices,
IEEE Trans. on Signal Process.
, 46, No. 11, pp. 3136-3139, (1998).
35. G. H. Golub and C. F. Van Loan,
Matrix computations
, 3rd ed., the Johns Hopkins
University Press, 1996.
36. S. Haykin,
Adaptive filter theory
, Englewoods Cliffs, NJ: Prentice Hall, 1991.
37. R. A. Horn and C. R. Johnson,
Matrix analysis
, Cambridge University Press, 1985.
38. Y. Hua, Y. Xiang, T. Chen, K. Abed Meraim, and Y. Miao, A new look at the power method
for fast subspace tracking,
Digital Signal Process.
, 9, No. 2, pp. 297-314, (1999).
39. B. H. Juang, S. Y. Kung, and C. A. Kamm (Eds.),
Proc. IEEE Workshop on neural net-
works for signal processing
, Princeton, NJ, September 1991.
40. I. Karasalo, Estimating the covariance matrix by signal subspace averaging,
IEEE Trans. on
on ASSP
, 34, No. 1, pp. 8-12, (1986).
41. J. Karhunen and J. Joutsensalo, Generalizations of principal componant analysis, optimiz-
ations problems, and neural networks,
Neural Networks
, 8, pp. 549-562, (1995).
42. S. Y. Kung and K. I. Diamantaras, Adaptive principal component extraction (APEX)
and applications,
IEEE Trans. on ASSP
, 42, No. 5, pp. 1202-1217, (1994).
43. H. J. Kushner and D. S. Clark,
Stochastic approximation for constrained and unconstrained
systems
, Applied math. Science, No. 26, Springer Verlag, New York, 1978.
44. H. J. Kushner,
Weak convergence methods and singular perturbed stochastic control and
filtering problems
, Vol. 3 of Systems and Control: Foundations and applications,
Birkh¨user, 1989.
45. A. P. Liavas, P. A. Regalia, and J. P. Delmas, Blind channel approximation: Effective
channel order determination,
IEEE Transactions on Signal Process.
, 47, No. 12,
pp. 3336-3344, (1999).
46. A. P. Liavas, P. A. Regalia, and J. P. Delmas, On the robustness of the linear prediction
method for blind channel identification with respect to effective channel undermodeling
/
overmodeling,
IEEE Transactions on Signal Process.
, 48, No. 5, pp. 1477-1481, (2000).
47. J. R. Magnus and H. Neudecker,
Matrix differential calculus with applications in statistics
and econometrics
, Wiley series in probability and statistics, 1999.
48. Y. Miao and Y. Hua, Fast subspace tracking and neural learning by a novel information
criterion,
IEEE Trans. on Signal Process.
, 46, No. 7, pp. 1967-1979, (1998).
49. E. Moulines, P. Duhamel, J. F. Cardoso, and S. Mayrargue, Subspace methods for the
blind identification of multichannel FIR filters,
IEEE Trans. Signal Process.
, 43, No. 2,
pp. 516-525, (1995).
50. E. Oja, A simplified neuron model as a principal components analyzer,
J. Math. Biol.
, 15,
pp. 267-273, (1982).
51. E. Oja and J. Karhunen, On stochastic approximation of the eigenvectors and eigenvalues
of the expectation of a randommatrix,
J. Math. anal. Applications
, 106, pp. 69-84, (1985).
52. E. Oja,
Subspace methods of pattern recognition
, Letchworth, England, Research Studies
Press and John Wiley and Sons, 1983.
53. E. Oja, Principal components, minor components and linear neural networks,
Neural net-
works
, 5, pp. 927-935, (1992).
Search WWH ::
Custom Search