Aircraft Take-off and Landing Performance Intelligent Computation Model and Applications

A novel aircraft take-off and landing performance intelligent computation model was proposed in this paper. First, main factors towards aircraft tableland take-off and landing performance were briefly analyzed. Then, general model for aircraft tableland take-off and landing performance computation was proposed. Finally, support vector machines (SVM) was applied in real taxing distance data of some type of aircraft. Meantime, so as to show superiority, high-precision etc. virtues of SVM, Bayesian regularized BP neural networks (BRBP), radical basis function neural networks (RBF), adaptive network based fuzzy inference systems (ANFIS) were also investigated. Results show that SVM is well suited for practical engineering use with satisfied generalized performance, as is better than other intelligent computation methods in the case studied in this paper. The proposed intelligent computation model is especially commendable for developing and enriching aircraft take-off and landing performance computation theory