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Yu. S. Shevnina, A. A. Vinokurov, M. A. Petrova, K. S. Nikolaev, K. A. Kriushkin Forecasting the Change in the State of a Nonlinear System Using Cognitive Maps |
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Abstract.
The article considers an approach to forecasting changes in the state of complex nonlinear systems with multiple granularity using multirelational fuzzy cognitive maps. A mathematical model of such a cognitive map is presented, as well as an algorithm for its construction, which includes multivariate nonlinear Hebb learning using multirelational data resources and a continuous genetic algorithm. The algorithm for constructing a multirelational fuzzy cognitive map allows one to determine cause-and-effect relationships between the states of components with high granularity and the states of components with low granularity. The application of the proposed approach to forecasting changes in the state of complex nonlinear systems for modeling the behavior of a coastal ecosystem anywhere in the world ocean is shown. The input parameters for the algorithm for constructing a multirelational fuzzy cognitive map of a coastal ecosystem and the limitations for multivariate nonlinear Hebb learning are determined. The results of experimental studies that were carried out for three sets of initial data are presented.
Keywords:
fuzzy cognitive maps, nonlinear Hebb learning, continuous genetic algorithm, high granularity, proper granularity, multirelational systems.
DOI 10.14357/20718632260103
EDN JAJDRD
PP. 28-40.
References
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