REFERENCES - Neural-based Orthogonal Data Fitting: The EXIN Neural Networks - page 231

68. R.D. Gitlin, H.C. Meadors, and S.B. Weinstein. The tap-leakage algorithm: an algo-
rithm for the stable operation of a digitally implemented, fractionally adaptive spaced
equalizer. Bell Syst. Tech. J ., 61(8):1817-1839, Oct. 1982.
69. L.J. Gleser. Calculation and simulation in errors-in-variables regression problems.
Mimeograph Ser. 78-5. Statistics Department, Purdue University, West Lafayette,
IN, 1978.
70. L.J. Gleser. Estimation in a multivariate “errors in variables” regression model: large
sample results. Ann. Stat ., 9:24-44, 1981.
71. G.H. Golub. Some modified eigenvalue problems. SIAM Rev ., 15:318-344, 1973.
72. G.H. Golub, A. Hoffman, and G.W. Stewart. A generalization of the Eckart-
Young-Mirsky matrix approximation theorem. Linear Algebra Appl ., 88-89:
322-327, 1987.
73. G.H. Golub and R.J. Leveque. Extensions and uses of the variable projection algo-
rithm for solving nonlinear least squares problems. Proceedings of the 1979 Army
Numerical Analysis and Computers Conference , White Sands Missile Range, White
Sands, NM, pp. 1-12, 1979.
74. G.H. Golub and C.F. Van Loan. An analysis of the total least squares problem. SIAM
J. Numer. Anal ., pp. 883-893, 1980.
75. G.H. Golub and C.F. Van Loan. Matrix Computations , 2nd ed. Johns Hopkins Uni-
versity Press, Baltimore, 1989.
76. G.H. Golub and C. Reinsch. Singular value decomposition and least squares solu-
tions. Numer. Math ., 14:403-420, 1970.
77. J.W. Griffiths. Adaptive array processing, a tutorial. IEE Proc. F , 130:3-10, 1983.
78. R. Haimi-Cohen and A. Cohen. Gradient-type algorithms for partial singular value
decomposition. IEEE Trans. Pattern Anal. Machine Intell ., 9(1):137-142, Jan. 1987.
79. H.R. Hall and R.J. Bernhard. Total least squares solutions to acoustic boundary ele-
ment models. In R.J. Bernhard and R.F. Keltie, eds., Proceedings of the International
Symposium on Numerical Techniques in Acoustic Radiation, ASME Winter Annual
Meeting , San Francisco, Vol. 6, pp. 145-152. American Society of Mechanical
Engineers, New York, 1989.
80. F.R. Hampel, E.N. Ronchetti, P. Rousser, and W.A. Stachel. Robust Statistics: The
Approach Based on Influence Functions . Wiley, New York, 1987.
81. M. Hassoun. Fundamentals of Artificial Neural Networks . MIT Press, Cambridge,
MA, 1995.
82. S. Haykin. Adaptive Filter Theory , 2nd ed. Prentice Hall, Englewood Cliffs, NJ,
1991.
83. S. Haykin. Neural Networks: A Comprehensive Foundation . IEEE Press, Piscataway,
NJ, 1994.
84. U. Helmke and J.B. Moore. Optimization and Dynamical Systems . Communications
and Control Engineering. Springer-Verlag, London, 1994.
85. M. Hestenes. Conjugate Direction Methods in Optimization . Springer-Verlag, New
York, 1980.
86. M.R. Hestenes and E. Stiefel. Methods of conjugate gradients for solving linear
systems. J. Res. Natl. Bur. Stand ., 49(6):409-436, 1952.
87. K. Hirakawa and T.W. Parks. Image denoising using total least squares. IEEE Trans.
Image Process ., 15:2730-2742, 2006.