By Topic

An evolution-oriented learning algorithm for the optimal interpolative net

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

2 Author(s)
S. -K. Sin ; Dept. of Electr. & Comput. Eng., California Univ., Irvine, CA, USA ; R. J. P. deFigueiredo

An evolution-oriented learning algorithm is presented for the optimal interpolative (OI) artificial neural net proposed by R. J. P. deFigueiredo (1990). The algorithm is based on a recursive least squares training procedure. One of its key attributes is that it incorporates in the structure of the net the smallest number of prototypes from the training set T necessary to correctly classify all the members of T. Thus, the net grows only to the degree of complexity that it needs in order to solve a given classification problem. It is shown how this approach avoids some of the difficulties posed by the backpropagation algorithm because of the latter's inflexible network architecture. The performance of this new algorithm is demonstrated by experiments with real data, and comparisons with other methods are also presented

Published in:

IEEE Transactions on Neural Networks  (Volume:3 ,  Issue: 2 )