A feedback analysis of perceptron learning for neural networks

Conference Record - Asilomar Conference on Signals, Systems and Computers

Volumn 2, Issue , 1995, Pages 894-898

  Sayed, A H ^a   Rupp, M ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b AT AND T LABS RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING ALGORITHMS; TIME DOMAIN ANALYSIS; UNCERTAINTY ANALYSIS;

FASTER CONVERGENCE; FEEDBACK ANALYSIS; FEEDBACK CONFIGURATION; FEEDBACK STRUCTURE; MEAN VALUE THEOREM; PERCEPTRON LEARNING; REFERENCE SIGNALS; SMALL GAIN THEOREM;

FEEDBACK;

EID: 0042655363     PISSN: 10586393     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ACSSC.1995.540829     Document Type: Conference Paper

References (7)

1. S. Haykin, Neural Networks: A Comprehensive Foundation, MacMillan Publishing Company, 1994.
2. S. Hui, S.H. Zak, "The Widrow-Hoff algorithm for McCulloch-Pitts type neurons," IEEE Trans. Neural Networks, vol. 5, no. 6, pp. 924-929, Nov. 1994.
3. D.R. Hush, B.G. Home, "Progress in supervised neural networks," IEEE Signal Processing Magazine, vol. 10, no. 1, pp. 8-39, Jan. 1993.
4. H. K. Khalil, Nonlinear Systems, MacMillan, 1992.
5. R.P. Lippmann, "An introduction to computing with neural nets," IEEE Acoustics, Speech and Signal Processing Magazine, vol. 4, no. 2, pp. 4-22, April 1987.
6. A.H. Sayed and M. Rupp, "A time-domain feedback analysis of adaptive gradient algorithms via the Small Gain Theorem", Proc. SPIE Conference on Advanced Signal Processing, vol. 2563, pp. 458-469, San Diego, CA, July 1995.
7. J.J. Shynk and N.J. Bershad, "On the system identification convergence model for perceptron learning algorithms," Proc. of Asilomar Conference on Signals, Systems, and Computers, pp. 879-886, Oct. 1994.