Designing and simulation of optoelectronic neural networks with the help of equivalence models and multivalued logics

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

doi:10.1117/12.258126

11 November 1996 Designing and simulation of optoelectronic neural networks with the help of equivalence models and multivalued logics

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

Author Affiliations +

Proceedings Volume 2824, Adaptive Computing: Mathematical and Physical Methods for Complex Environments; (1996) https://doi.org/10.1117/12.258126
Event: SPIE's 1996 International Symposium on Optical Science, Engineering, and Instrumentation, 1996, Denver, CO, United States

Abstract

The theory and equivalental models of neural networks based on equivalence operation of continuous and multivalued neural logic are considered. Their connection with metric of metric-address spaces are shown. Normalized equivalences 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 being most generalized can also conduct the recalculation process of networks to two-step algorithms without calculation of connections matrix. Equivalent models require calculations based on vector-matrix procedures with equivalence operation and can be realized on vector-matrix 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.

Citation Download Citation

Vladimir G. Krasilenko, Anatoly K. Bogukhvalsky, and Andrey T. Magas "Designing and simulation of optoelectronic neural networks with the help of equivalence models and multivalued logics", Proc. SPIE 2824, Adaptive Computing: Mathematical and Physical Methods for Complex Environments, (11 November 1996); https://doi.org/10.1117/12.258126

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