Abstract-This paper proposes a genetic-algorithm-based method for selecting a small number of significant fuzzy if-then rules to construct a compact fuzzy classification system with high classification power. The rule selection problem is formulated as a combinatorial optimization problem with two objectives: to maximize the number of correctly classified patterns and to minimize the number of fuzzy if-then rules. Genetic algorithms are applied to this problem. A set of fuzzy if-then rules is coded into a string and treated as an individual in genetic algorithms. The fitness of each individual is specified by the two objectives in the combinatorial optimization problem. The performance of the proposed method for training data and test data is examined by computer simulations on the iris data of Fisher.
In view of the deficiencies of poor prediction accuracy, time-consuming and low efficiency of traditional traffic prediction models, fuzzy constraints are introduced into air traffic traffic system to represent some uncertain information in the field of artificial intelligence, and construct a fuzzy constraint-based air traffic flow prediction The fuzzy constraint-based air traffic prediction model is constructed. By analyzing the decision vector, fuzzy parameter vector and fuzzy constraint set, the prediction model is proposed. The air traffic flow prediction model is built by analyzing the decision vector, fuzzy parameter vector and fuzzy constraint set that affect the fuzzy constraint, and proposing the construction process of the prediction model. The experimental results show that the air traffic flow prediction model can be used to predict the air traffic flow. The experimental results show that the improved prediction model is better than the traditional prediction model in predicting air traffic flow. The results show that the improved prediction model has better prediction results, shorter time consumption and higher accuracy than the traditional prediction model.
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