Learning algorithm for total least-squares adaptive signal processing

Electronics Letters

Volumn 28, Issue 4, 1992, Pages 430-432

  Gao, K ^a   Ahmad, M O ^a   Swamy, M N S ^a  

^a Dept of Electrical and Computer Engineering   (Canada)

Author keywords

Neural networks; Signal processing

Indexed keywords

CONTROL SYSTEMS, ADAPTIVE; ELECTRIC FILTERS, DIGITAL; LEARNING SYSTEMS; MATHEMATICAL TECHNIQUES; SIGNAL PROCESSING;

ADAPTIVE SIGNAL PROCESSING; FIR FILTERING; IIR FILTERING; LEARNING ALGORITHMS; LMS ALGORITHMS; RLS ALGORITHMS;

COMPUTER PROGRAMMING;

EID: 0026821218     PISSN: 00135194     EISSN: None     Source Type: Journal    
DOI: 10.1049/el:19920270     Document Type: Article

References (8)

1. GOLUB, G. H., and VAN LOAN, C. F.: ‘An analysis of the total least squares problem’, SIAM J. Numer. Anal., 1980, 17, (6), pp. 883-893
2. OJA, E.: ‘A simplified neuron model as a principal component analyzer’, J. Math. Biology, 1982,15, pp. 267-273
3. XU, L., OJA, E., and SUEN, C. Y.: ‘Modified Hebbian learning for curve and surface fitting’. Technical Report, No. X9121, Dept. of Computer Science, Concordia University, 1991
4. ROBBINS, H., and MONRO, S.: ‘A stochastic approximation method’, Annals of Mathematical Statistics, 1977, 22, pp. 400-407
5. LJUNG, L.: ‘Analysis of recursive stochastic algorithms’, IEEE Trans., 1977, ACC-22, (4), pp. 551-575
6. WIDROW, B.: ‘Adaptive signal processing’ (Prentice-Hall, Englewood Cliffs, NJ, 1985)
7. SHYNK, J. J.: ‘Adaptive IIR filtering’, IEEE ASSP Magazine, 1989, 2, pp. 4-21
8. HAYKIN, S.: ‘Adaptive filter theory’ (Prentice-Hall, Englewood Cliffs, NJ, 1986)