The article discusses the use of a vaguely defined knowledge base and neural networks for risk assessment when using complex poorly formalized systems. An example of the risk assessment task is the operation of a storage facility for the transport or storage of industrial products. Consideration of a fire with different parameters is required as an anomalous external influence. The storage response, measured by the assessment of the safety of the content and its non-penetration into the external environment, can vary depending on the current state of the system, for example, the degree of possible damage in emergency conditions.
At present, a significant part of optimization problems, particularly questions of combinatorial optimization, are considered NP-complete problems. When solving optimization problems, the neural network approach increases the probability of obtaining an optimal solution. The traveling salesman problem is considered a test optimization problem. This problem was solved using the Hopfield neural network. In solving optimization problems, numerous computation processes and computation time are required. To improve performance and increase the program's speed, there are cases of inappropriate purchase of additional programs and tools, and involvement of additional services. In these cases, parallel computing technologies are used to give an effective result. Based on the developed algorithms, several computational experiments were carried out. The analysis of the obtained results showed that the algorithms of artificial neural networks proposed by us, in comparison with the algorithms created based on Hopfield neural networks, are characterized by low resource consumption and efficiency in terms of high speed. But, it should be noted that if the volume of tasks is very large, neural network algorithms may become less efficient due to longer computation. In such cases, it is usually advisable to use evolutionary algorithms. In particular, the study considers using the bee swarm algorithm for parallel computing technologies. Solving optimization problems using the bee swarm algorithm in parallel computing technologies can be significantly efficient and fast.
Today, in public transport planning systems, it is relevant to a search for a possible route with a minimum time. The aim of the work is the development of intelligent algorithms for constructing public transport routes, the development of programs, and the conduct of a computational experiment. Research methods are the theory of neural networks. The paper considers Hopfield neural networks and proposed recurrent neural networks. However, in Hopfield neural networks, the chances of solving this optimization problem decrease as the matrix size increases. A recurrent neural network is proposed, represented by a differential equation to solve this problem. As a result, the number of iterative computations can be reduced by n2 times than in the Hopfield network.
The aim of the research is to study the models, rules, and fuzzy inference engines, which occupy the main place in the knowledgebase, and models of the logic inference engines and simulation modeling, focused on supporting the adoption of semi-structured decisions under uncertainty. This implies the relevance of the task of developing theoretical and methodological tools that provide automation of the processes of fuzzy inference systems. Research methods are the theory of fuzzy sets and fuzzy logic. New scientific results are the design and formation of a set of production rules from a given set of admissible ones, with specific values of conditions and conclusions for describing three types of fuzzy models of the processes and tasks under study. Using modules of standard algorithms and programs, algorithms and a program for solving problems of fuzzy inference systems and making semi-structured decisions based on the constructed fuzzy logic model were developed. This problem is solved by formalization methods based on the theory of algorithmization, fuzzy sets, and fuzzy inference.
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