基于稀疏最小二乘支持向量机的航空发动机动态过程辨识

针对现有最小二乘支持向量机(LS-SVM)稀疏性不足的难题,提出一种稀疏化策略,应用此方法建立了航空发动机动态过程模型.在对原始样本预求解过程中,该策略使用改进Gram-Schmidt正交化算法对非线性映射矩阵实施递归分解,同时以阈值监督输出向量的残差化过程,从而优选训练样本,降低样本规模,节省内存,提高LS-SVM学习速度.仿真表明,基于优选样本的学习模型较之其他训练样本学习模型提高了回归精度和速度,验证了方法的可行性;基于实际试验数据建立的航空发动机动态过程模型在类似过程参数预测以及性能递推预估仿真表明,高压转子相对转速误差低于0.2%,低压转子相对转速误差低于0.35%,涡轮后燃气温度误差小于3.5℃,满足控制与仿真的需要. 

Dynamic process identification of aircraft engine based on a novel sparse least square support vector machines

A novel sparse strategy was proposed for the least square support vector machines regression,and the dynamic process identification of aircraft engine was analyzed in particular.In the process of solving the original sample,the special nonlinear mapping matrix was recursively decomposed by using the modified Gram-Schmidt method,and the excellent sample was selected when the square of remaining output reached the threshold value;thus,the new sample quantity was reduced and the least square support vector machines(LS-SVM) regression model training speed was enhanced by saving memory.The simulation result shows that,the algorithm has better sparseness,less training time and equivalent precision;and the aircraft engine dynamic process simulation shows that the predicting error of high rotor's relative rotational speed is less than 0.2%,the predicting error of the low rotor's relative rotational speed is less than 0.35%,and the predicting error of the temperature at the exit of high-pressure turbine is less than 3.5 ℃.The model is suitable for aircraft engine's control and system simulation.