An adaptive approach for optimal data reduction using recursive least squares learning method

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn 2, Issue , 1992, Pages 297-300

  Bannour, S ^a   Azimi Sadjadi, M R ^a  

^a Veterinary Dentistry and Oral Surgery   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUDIO SIGNAL PROCESSING; POSITION CONTROL; PRINCIPAL COMPONENT ANALYSIS; RANDOM PROCESSES; SIGNAL PROCESSING; SPEECH COMMUNICATION; STOCHASTIC SYSTEMS;

ADAPTIVE APPROACH; NEW APPROACHES; PRINCIPAL COMPONENTS; RECURSIVE COMPUTATION; RECURSIVE LEAST SQUARE (RLS); SINGLE LAYER PERCEPTRON;

LEAST SQUARES APPROXIMATIONS;

EID: 84944990594     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.1992.226061     Document Type: Conference Paper

References (4)

1. Oja, E.: "A simplified neuron model as a principal component analyzer," J. Math. Biology 15:267-273, 1982.
2. Sanger, T. D.: "Optimal unsupervised learning in a single-layer linear feedforward neural network," Neural Networks, Vol. 2, pp. 453-473, 1989.
3. Kung, S. Y and Diamantaras, K. I.: "A neural network algorithm for adaptive principal component extraction," Proc. of IEEE Inter. Conf. on Acoustics, Speech and Signal Processing (ICASSP'90), pp. 861-864, New Mexico, April 1990.
4. M. R. Azimi-Sadjadi, S. Citrin and S. Sheedvash: "Supervised learning process of multi-layer perceptron networks using fast recursive least-squares," Proc. ICASSP'90, pp. 1381-1384, New Mexico, April 1990.