Equivalence models (EM) advantages of neural networks (NN) are shown in paper. EMs are based on vectormatrix procedures with basic operations of continuous neurologic: normalized vector operations "equivalence", "nonequivalence", "autoequivalence", "autononequivalence". The capacity of NN on the basis of EM and of its modifications, including auto-and heteroassociative memories for 2D images, exceeds in several times quantity of neurons. Such neuroparadigms are very perspective for processing, recognition, storing large size and strongly correlated images. A family of "normalized equivalence-nonequivalence" neuro-fuzzy logic operations on the based of generalized operations fuzzy-negation, t-norm and s-norm is elaborated. A biologically motivated concept and time pulse encoding principles of continuous logic photocurrent reflexions and sample-storage devices with pulse-width photoconverters have allowed us to design generalized structures for realization of the family of normalized linear vector operations "equivalence"-"nonequivalence". Simulation results show, that processing time in such circuits does not exceed units of micro seconds. Circuits are simple, have low supply voltage (1-3 V), low power consumption (milliwatts), low levels of input signals (microwatts), integrated construction, satisfy the problem of interconnections and cascading.

Date Published: 12 April 2010
PDF: 11 pages
Proc. SPIE 7703, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VIII, 77030P (12 April 2010); doi: 10.1117/12.850871

Published in SPIE Proceedings Vol. 7703:
Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VIII
Harold H. Szu; F. Jack Agee, Editor(s)