Geodesic
Analysis of computational complexity of federated algorithms for neurocognitive control of imitation phenogenetic models of plants
News of the Kabardin-Balkar scientific center of RAS, Tome 26 (2024) no. 5, pp. 107-128.

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The aim of the study is to develop a methodology for creating hybrids of economically useful plants with a given set of phenotypic properties based on the use of universal artificial intelligence methods for managing federated simulation models of vegetation. The main objective of this work is to analyze the computational complexity of the main algorithms for the functioning and training of neurocognitive systems for managing federated simulation models of plant vegetation using computers of various types. The paper presents the results of estimating the execution time of the dispatching cycle in a federated system for imitation modeling of plant phenogenetic dynamics on a sequential and parallel computer.
Keywords: universal artificial intelligence, multi-agent systems, neurocognitive control, plant breeding, gene expression, computational complexity analysis, federated algorithms 
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     title = {Analysis of computational complexity of federated algorithms for neurocognitive control of imitation phenogenetic models of plants},
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M. A. Abazokov; M. I. Anchekov; K. Ch. Bzhikhatlov; Zh. H. Kurashev; Z. V. Nagoev; O. V. Nagoeva; A.A. Unagasov; A.A. Khamov. Analysis of computational complexity of federated algorithms for neurocognitive control of imitation phenogenetic models of plants. News of the Kabardin-Balkar scientific center of RAS, Tome 26 (2024) no. 5, pp. 107-128. http://geodesic.mathdoc.fr/item/IZKAB_2024_26_5_a8/

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