A Caltech Library Service

A generalized convergence theorem for neural networks

Bruck, Jehoshua and Goodman, Joseph W. (1988) A generalized convergence theorem for neural networks. IEEE Transactions on Information Theory, 34 (5). pp. 1089-1092. ISSN 0018-9448. doi:10.1109/18.21239.

See Usage Policy.


Use this Persistent URL to link to this item:


A neural network model is presented in which each neuron performs a threshold logic function. The model always converges to a stable state when operating in a serial mode and to a cycle of length at most 2 when operating in a fully parallel mode. This property is the basis for the potential applications of the model, such as associative memory devices and combinatorial optimization. The two convergence theorems (for serial and fully parallel modes of operation) are reviewed, and a general convergence theorem is presented that unifies the two known cases. New relations between the neural network model and the problem of finding a minimum cut in a graph are obtained.

Item Type:Article
Related URLs:
URLURL TypeDescription
Bruck, Jehoshua0000-0001-8474-0812
Additional Information:© Copyright 1988 IEEE. Reprinted with permission. Manuscript received June 1987; revised November 1987. This work was supported in part by the Rothschild Foundation and by the U.S. Air Force Office of Scientific Research. This correspondence was presented in part at the IEEE First International Conference on Neural Networks, San Diego, CA, June 1987.
Issue or Number:5
Record Number:CaltechAUTHORS:BRUieeetit88
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:5705
Deposited By: Archive Administrator
Deposited On:29 Oct 2006
Last Modified:08 Nov 2021 20:28

Repository Staff Only: item control page