Scientific method apparatus for intellectual assessment of the state of complex systems

In this section of the research, a scientific and method apparatus for intelligent assessment of the state of complex systems is proposed. The basis of this research is the theory of artificial intelligence, namely evolving artificial neural networks, basic genetic algorithm procedures, neuro-fuzzy expert systems and bio-inspired algorithms.

In the course of the research, the authors proposed:
– the method approach to assessing the state of hierarchical systems using a metaheuristic algorithm;
– the method of analysis and forecasting of the state of multidimensional objects using a metaheuristic algorithm;
– the method of increasing the reliability of the assessment of the object state.

The use of the proposed scientific and method apparatus will allow:
– to reduce the probability of premature convergence of the metaheuristic algorithm;
– to maintain a balance between the speed of convergence of the metaheuristic algorithm and diversification;
– to take into account the type of uncertainty and noisy data of the metaheuristic algorithm;
– to take into account the available computing resources of the state analysis system of the analysis object;
– to take into account the priority of search by swarm agents of the metaheuristic algorithm;
– to carry out the initial display of flock individuals taking into account the uncertainty type;
– to conduct accurate training of individuals of metaheuristic algorithms;
– to conduct a local and global search taking into account the degree of noise of the data on the state of the analysis object;
– to apply as a universal tool for solving the task of analyzing the state of analysis objects due to the hierarchical description of analysis objects;
– to check the reliability of the obtained results;
– to increase the reliability of the assessment of the objects state of analysis due to the construction of object and relational models of their state with different degrees of hierarchy;
– to avoid the local extremum problem.