Published April 30, 2023 | Version v1
Journal article Open

Development of a method of complex analysis and multidimensional forecasting of the state of intelligence objects

Authors/Creators

  • 1. National Aviation University
  • 2. Military Institute of Telecommunications and Information Technologies named after Heroes of Kruty
  • 3. Research Center for Trophy and Perspective Weapons and Military Equipment
  • 4. Central Scientifically-Research Institute of Armaments and Military Equipment of the Armed Forces of Ukraine
  • 5. Scientific-Research Institute of Military Intelligence

Description

A method of complex analysis and multidimensional forecasting of the state of intelligence objects is proposed to increase the accuracy of their state assessment. The object of research is decision support systems. The subject of research is the process of decision-making in management problems using artificial intelligence methods. The hypothesis of research is to increase the efficiency of decision-making with a given assessment reliability. The proposed method is based on a combination of fuzzy cognitive and temporal models, an advanced cat swarm optimization algorithm and evolving artificial neural networks. The method has the following sequence of actions:

‒ input of initial data;

‒ processing of initial data taking into account uncertainty about the state of heterogeneous intelligence objects;

‒ construction of a fuzzy temporal ontological model of heterogeneous intelligence objects;

‒ conclusion on the state of heterogeneous intelligence objects;

‒ correction of the fuzzy temporal ontological model;

‒ building a fuzzy relational temporal cognitive model of heterogeneous intelligence objects and forecasting the state of the intelligence object;

‒ training knowledge bases on heterogeneous intelligence objects.

The training procedure consists in learning the synaptic weights of the artificial neural network, the type and parameters of the membership function, as well as the architecture of individual elements and the architecture of the artificial neural network as a whole. The method makes it possible to increase the efficiency of data processing at the level of 18–25 % by using additional improved procedures. The proposed method should be used to solve the problems of evaluating complex and dynamic heterogeneous intelligence objects, characterized by a high degree of complexity.

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