Equivalent models of neural networks and their effective optoelectronic implementations based on matrix multivalued elements

Vladimir G. Krasilenko; Anatoly K. Bogukhvalskiy; Andrey T. Magas

doi:10.1117/12.267699

28 February 1997 Equivalent models of neural networks and their effective optoelectronic implementations based on matrix multivalued elements

Vladimir G. Krasilenko, Anatoly K. Bogukhvalskiy, Andrey T. Magas

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Proceedings Volume 3055, International Conference on Optical Storage, Imaging, and Transmission of Information; (1997) https://doi.org/10.1117/12.267699
Event: International Conference on Optical Storage, 1996, Kiev, Ukraine

Abstract

The theory and equivalental models of neural networks based on equivalence operation (non-equivalence) of continuous and multivalued neural logic are considered. Their connection with metric of metric-address spaces are shown. Normalized equivalencies of vectors with multilevel components are determined. Equivalental models for simple network with weighted correlation coefficients, for network with adapted weighing and double weighing are suggested. It is shown, that the network model with double weighing (adapted and correlation coefficients) being most generalized can also conduct the recalculation process of networks to two-step algorithms without calculation of connections matrix. Equivalental models require calculations based on vector- matrix procedures with equivalence operation and can be realized on vector-matrix equivalentors with space and time integration. The apparatus implementations of models with productivity of 10⁸ divided by 10⁹ connections/sec and neuron number 256 and more are suggested.

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Vladimir G. Krasilenko, Anatoly K. Bogukhvalskiy, and Andrey T. Magas "Equivalent models of neural networks and their effective optoelectronic implementations based on matrix multivalued elements", Proc. SPIE 3055, International Conference on Optical Storage, Imaging, and Transmission of Information, (28 February 1997); https://doi.org/10.1117/12.267699

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KEYWORDS

Neural networks

Logic

Neurons

Optoelectronics

Detection and tracking algorithms

Logic devices

Chemical elements

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