基于粗糙集与最小二乘支持向量机的飞机综合智能论证

Study on Intelligent Evaluation for Airplane Design based on RS and LS-SVM

结合粗糙集的属性约简理论与最小二乘回归支持向量机的回归思想,提出了一种基于粗糙集与最小二乘回归支持向量机的飞机设计综合智能论证模型。首先根据历史数据建立属性决策表,然后应用粗糙集理论对飞机综合论证指标参数属性进行约简来获得影响飞机设计综合论证的核心指标,最后再利用支持向量机回归模型建立与飞机综合论证核心因素之间的非线性映射模型来对飞机的作战效能进行预测。仿真实例验证了该方法可以降低模型的复杂度,加快SVM的训练速度并具有良好的预测效果。

Based on the idea of reduction of attribute of Rough Set（RS）and regression of the Least Squares Support Vector Machine（LS-SVM）,a kind of evaluation model of airplane design based on RS and LS-SVM has been presented.Firstly,the attributes decision table was built based on the history data.Secondly,the parameter indexes of intelligent evaluation of airplane design were reduced with RS and the core indexes which effective the intelligent evaluation for airplane plane were found.Lastly,the non-linearity mapping model between the mostly factors of airplane intelligent evaluation and SVM is established and the cost forecasting is performed.The simulation example shows that the method can reduce the complexity of SVM model,expedite the train speed of SVM and the forecast result is favorable.