Continuous logic equivalence models of Hamming neural network architectures with adaptive-correlated weighting

Vladimir G. Krasilenko; Felix M. Saletsky; Victor I. Yatskovsky; Karim Konate

doi:10.1117/12.304973

1 April 1998 Continuous logic equivalence models of Hamming neural network architectures with adaptive-correlated weighting

Vladimir G. Krasilenko, Felix M. Saletsky, Victor I. Yatskovsky, Karim Konate

Author Affiliations +

Proceedings Volume 3402, Optical Information Science and Technology (OIST97): Optical Memory and Neural Networks; (1998) https://doi.org/10.1117/12.304973
Event: Optical Information Science and Technology, 1997, Moscow, Russian Federation

Abstract

The continuous logic `equivalental' models of Hamming neural networks with adaptive-correlated weighting and multiport associative memory based on equivalence operation of neural logic are considered. The models for simple network with weighted correlation coefficient, for network with adapted weighting and double weighting and their system equivalental functions are suggested. The models require calculations based on two-step algorithms and vector-matrix procedures with the normalized equivalence operation. Modified equivalence models of neural networks and associative memory for space-invariant 2D pattern recognition are proposed. Possible variants of the models implementation are considered. Neural networks architecture for invariant 2D pattern recognition consist of equivalentors, every of which replace two correlators.

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Vladimir G. Krasilenko, Felix M. Saletsky, Victor I. Yatskovsky, and Karim Konate "Continuous logic equivalence models of Hamming neural network architectures with adaptive-correlated weighting", Proc. SPIE 3402, Optical Information Science and Technology (OIST97): Optical Memory and Neural Networks, (1 April 1998); https://doi.org/10.1117/12.304973

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