Subtitle:

Książka = Book ; KS/1/2003/T33R05P07

Publisher:

Instytut Badań Systemowych. Polska Akademia Nauk ; Systems Research Institute. Polish Academy of Sciences

Place of publishing:

Warszawa

Description:

[4], 325-336 pages ; 21 cm ; Bibliography p. 336

Type of object:

Book/Chapter

Abstract:

In this paper, hardware implemented artificial neural networks (ANN's) capable of learning on silicon are considered. The ability to learn within a chip means that the network can fast adapt to varying conditions during the recall phase, i.e. can learn in operation. This is impossible in classical ANN's. Thinking about building such adaptive networks became realistic only recently due to advances in CMOS processes. An important and difficult task in hardware implementations of the ANN's is to find proper solutions for analog circuits that can play a role of basie network elements such as synapses, analog [ocal memories and activation Junction circuits. Concrete realizations of these circuits have been presented. The shown circuits were designed in the Institute of Telecommunication, ATR in Bydgoszcz, Poland. The performed experimental studies concern a prototype CMOS chip, fabricated by Nordic in the framework of EUROPRACTICE.

Relation:

Książka = Book

Detailed Resource Type:

Book

Resource Identifier:

oai:rcin.org.pl:159224

Source:

KS-2003-01-T33R05P07

Language:

pol

Language of abstract:

pol

Rights:

Creative Commons Attribution BY 4.0 license

Terms of use:

Copyright-protected material. [CC BY 4.0] May be used within the scope specified in Creative Commons Attribution BY 4.0 license, full text available at: ; -

Digitizing institution:

Systems Research Institute of the Polish Academy of Sciences

Original in:

Library of Systems Research Institute PAS

Projects co-financed by:

Operational Program Digital Poland, 2014-2020, Measure 2.3: Digital accessibility and usefulness of public sector information; funds from the European Regional Development Fund and national co-financing from the state budget.

Access:

Open