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Cybernetics and programming
Reference:

Optimization of cognitive maps used in forecasting

Zhilov Ruslan Al'berdovich

graduate student, Institute for Applied Mathematics

360000, Russia, Kabardino-Balkariya, g. Nal'chik, ul. Shortanova, 89a

kavkaze@inbox.ru

DOI:

10.7256/2306-4196.2015.5.16592

Review date:

08-10-2015


Publish date:

27-11-2015


Abstract: The subject of the study is the need for decision-making in the semi structured dynamic situations, when the parameters, the laws and regularities of the situation are described qualitatively. Those are unique situations, in which the dynamics of the parameters of the situation is combined with the difficulty of predicting changes in its structure. Object of research are cognitive maps or computer systems for modeling cognitive maps. The author considers in detail such aspects of the topics as the complexity of the decision making when the information is insufficient and inaccurate, methods of optimization for cognitive maps used in forecasting. Special attention is paid to the optimization of data dimension algorithm and optimization of the cognitive maps structure. To optimize the dimensions of the data the author uses cluster analysis methods, optimization of the structure of cognitive maps is performed by automatic changing of the weights of the impact of concepts to other machine learning methods. The main conclusion of the study is the decrease in subjectivism of the cognitive map in predicting system. Authors' contributions to the study of the subject is a representation of the cognitive map as a single-layer neural network and the application of methods of neural networks training, proposed by Rosenblatt. The novelty of the study is in the new structure of cognitive maps.


Keywords: cognitive map, concept, cluster analysis, neural network, machine learning, training set, fuzzy sets, dimension data, Euclidean distance, parameterization
This article written in Russian. You can find full text of article in Russian here .

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