Although the Belief Rule Base (BRB) established based on single attribute rules solves the problem of rule explosion, the dependency between attributes makes it impossible to accurately describe the relationship between attributes and results, resulting in low accuracy. For this problem, existing solutions are mostly limited by expert knowledge or only applicable to weakly correlated attributes. Therefore, a method of attribute clustering and intersection guided by particle swarm optimization is proposed, which integrates attributes linearly or non-linearly to obtain new attributes, which are used in the BRB model. This method can effectively solve the influence of attribute interdependence on BRB prediction results. Experiments show that the method has good accuracy in solving typical fault diagnosis, pattern recognition and other classification problems, and the number of rules has a linear relationship with the number of attributes and reference values, avoiding rule explosion.

Aiming at the problem that the advanced arresting device is difficult to obtain the landing speed of the aircraft in real time, this paper predicts the speed of the aircraft through the self-encoding fuzzy inference system (AE-ANFIS). Firstly, the working principle of the advanced arresting system is expounded, and the sensors directly related to the aircraft speed measurement are analyzed. And filter auxiliary variables through feature extraction and maximum information coefficient (MIC); then predict acceleration through adaptive fuzzy neural network (ANFIS); finally, for the problem of over-fitting caused by the large number of ANFIS rules, an auto-encoder (AE) method is proposed. Data dimensionality reduction is performed by extracting abstract features, which effectively improves the prediction accuracy of ANFIS. The experimental results show that the method proposed in this paper can fit the aircraft speed well, and the accuracy is better than traditional ANFIS and BP, LSTM, GoogleNet, AlexNet and other algorithms.