By Topic

Application of the Hopfield network in robust estimation of parametric membership sets for linear models

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
I. N. da Silva ; Dept. of Comput. Eng. & Ind. Autom., Univ. Estadual de Campinas, Sao Paulo, Brazil ; L. V. R. de Arruda ; W. C. do Amaral

High computation rates can be achieved using artificial neural networks. Optimization problems can be solved by neural networks with feedback connections by employing a massive number of simple processing elements with high degree of connectivity between these elements. In this paper, an application of Hopfield neural networks in robust parametric estimation with unknown-but-bounded disturbance is presented. The internal parameters of the Hopfield neural network are obtained using the valid-subspace technique. These parameters are explicitly computed to assure the network convergence. A comparative analysis with other robust estimation methods is carried out by a simulation example

Published in:

Systems, Man and Cybernetics, 1995. Intelligent Systems for the 21st Century., IEEE International Conference on  (Volume:5 )

Date of Conference:

22-25 Oct 1995