By Topic

Multi-valued and universal binary neurons: mathematical model, learning, networks, application to image processing and pattern recognition

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
$31 $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)
Aizenberg, N.N. ; Dept. of Cybernetics, Uzhgorod State Univ., Russia ; Aizenberg, I.N. ; Krivosheev, G.A.

Conception of universal binary neurons and multivalued neurons with complex-valued weights and their applications to image processing and pattern recognition are considered in this paper. First, efficiency of the “passage” to the complex domain for increasing of the neuron's functionality is considered. A solution of the XOR problem on the single universal binary neuron is considered. The high speed learning algorithm for the both neurons is developed. Next, neural networks with cellular and random connections based on the considered neurons are proposed. Applications of such networks to image processing (cellular) and image recognition (random) are proposed. The use of multi-valued neurons for time-series extrapolation is also considered

Published in:

Pattern Recognition, 1996., Proceedings of the 13th International Conference on  (Volume:4 )

Date of Conference:

25-29 Aug 1996