CAM-based ASOCS implementation

Author

Bartczak, Andrew ; Daly, James

Author_Institution

Dept. of Electr. Eng., Rhode Island Univ., Kingston, RI, USA

Volume

4

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

2103

Abstract

We describe an implementation of adaptive self-organizing concurrent system (ASOCS) using content addressable memory (CAM). ASOCS is an artificial neural network with supervised online learning trained by incrementally introduced Boolean propositional logic rules or instances. Words stored in CAM represent modified instances after they are processed by ASOCS to ensure consistency and minimality of the entire collection of instances. CAM permits fast training and execution. Main advantages of our implementation are simplicity, scalability to handle wider input and output vectors, ability to preset the network structure in advance based on software simulation

Keywords

Boolean functions; adaptive systems; content-addressable storage; learning (artificial intelligence); neural chips; neural nets; Boolean propositional logic rules; adaptive self-organizing concurrent system; content addressable memory; input vectors; neural network; output vectors; scalability; supervised online learning; Adaptive systems; Artificial neural networks; Associative memory; Boolean functions; CADCAM; Computer aided manufacturing; Hardware; Propagation delay; Scalability; Very large scale integration;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374539

Filename

374539