Abstract
Intelligent was very important for command decision model, and it was also the key to improve the quality of simulation training and combat experiment. The decision-making content was more complex in the implementation of tasks and the nature of the problem was different, so the demand for intelligence was high. To solve better the problem, this paper presented a game method and established a game neural network model. The model had been successfully applied in the classification experiment of winning rate between chess game, which had good theoretical significance and application value.
Highlights
Today, simulation training and combat experiments are increasingly demanding on command decision models
Intelligent was very important for command decision model, and it was the key to improve the quality of simulation training and combat experiment
To solve better the problem, this paper presented a game method and established a game neural network model
Summary
Simulation training and combat experiments are increasingly demanding on command decision models. Combat experiments need to solve the problem that the experimental credibility is not high and the space to think about is difficult to automatically explore. This poses a demand for the intelligence of the command decision model. Fuzzy automata [2]-[8] are powerful tools to deal with fuzzy feature information Based on this basis, this paper focuses on the establishment of target control system of fuzzy automata (FA). This paper will propose a game method, establish the game neural network model, and apply the model to the classification experiment of winning rate between chess
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