|
| This Article | ||
| ||
| Share | ||
| Bibliographic References | ||
| Add to: | ||
 Digg  Furl  Spurl  Blink  Simpy  Google  Del.icio.us  Y!MyWeb | ||
| Search | ||
| ||
| ASCII Text | x | ||
| Tao Wang, Xinhau Zhuang, XiaoLiang Xing, Xipeng Xiao, "A Neuron-Weighted Learning Algorithm and its Hardware Implementation in Associative Memories," IEEE Transactions on Computers, vol. 42, no. 5, pp. 636-640, May, 1993. | |||
| BibTex | x | ||
| @article{ 10.1109/12.223686, author = {Tao Wang and Xinhau Zhuang and XiaoLiang Xing and Xipeng Xiao}, title = {A Neuron-Weighted Learning Algorithm and its Hardware Implementation in Associative Memories}, journal ={IEEE Transactions on Computers}, volume = {42}, number = {5}, issn = {0018-9340}, year = {1993}, pages = {636-640}, doi = {http://doi.ieeecomputersociety.org/10.1109/12.223686}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Computers TI - A Neuron-Weighted Learning Algorithm and its Hardware Implementation in Associative Memories IS - 5 SN - 0018-9340 SP636 EP640 EPD - 636-640 A1 - Tao Wang, A1 - Xinhau Zhuang, A1 - XiaoLiang Xing, A1 - Xipeng Xiao, PY - 1993 KW - hardware implementation; associative memories; learning algorithm; neuron-weighted associative memory; NWAM; global minimization; gradient descent rule; analog neural network; computer simulation experiments; content-addressable storage; learning (artificial intelligence); neural chips; neural nets. VL - 42 JA - IEEE Transactions on Computers ER - | |||
A novel learning algorithm for a neuron-weighted associative memory (NWAM) is presented. The learning procedure is cast as a global minimization, solved by a gradient descent rule. An analog neural network for implementing the learning method is described. Some computer simulation experiments are reported.
[1] L. F. Abbott and T. B. Kepler, "Optimal learning in neural network memories,"J. Phys. A: Math. Gen., vol. 22, pp. L711-717, 1989.
[2] J. Bruck and V. P. Roychowdhury, "On the number of spurious memories in the Hopfield model,"IEEE Trans. Inform. Theory, vol. IT-36, no. 2, pp. 393-397, 1990.
[3] J. J. Hopfield, "Neural networks and physical systems with emergent collective computational abilities,"Proc. Nat. Acad. Sci. USA, vol. 79, 1982, pp. 2554-2558.
[4] M. P. Kennedy and L. O. Chua, "Neural networks for nonlinear programming,"IEEE Trans. Circuits Syst., vol. 35, no. 5, pp. 554-562, 1988.
[5] W. Krauth and M. Mezard, "Learning algorithms with optimal stability in neural networks,"J. Phys. A: Math. Gen., vol. 20, pp. L745-752, 1987.
[6] R. J. McEliece, E. C. Posner, E. R. Rodemich, and S. S. Venkatesh, "The capacity of the Hopfield associative memory,"IEEE Trans. Inform. Theory, vol. IT-33, pp. 461-482, 1987.
[7] A. Moopenn, J. Lambe, and A. P. Thankoor, "Electronic implementation of associative memory based on neural network models,"IEEE Trans. Syst., Man., Cybern., vol. SMC-17, no. 2, pp. 325-331, 1987.
[8] G. V. Vanderplaats,Numerical Optimization Techniques for Engineering Design: With Applications. New York: McGraw-Hill, 1984.
[9] A. R. Vazquez, R. D. Castro, A. Rueda, J. L. Huertas, and E. S. Sinencio, "Nonlinear switched-capacitor neural networks for optimization problems,"IEEE Trans. Circuits Syst., vol. 37, no. 3, pp. 384-397, 1990.
[10] C. F. Zhang, X. Q. Wang, Z. Q. Zhou, and K. H. Zhao, "Analysis of varieties of neural network models for associative memory,"J. Pattern Recognition Artif. Intell. China, vol. 3, no. 1, pp. 21-27, 1990.