航空发动机约简遗传规划起动非线性动态模型

提出了一种新的用于航空发动机最优化起动过程数据驱动动态建模的约简遗传规划(PGP)算法.这种模型采用遗传规划产生航空发动机辨识模型的输入输出非线性模型集,并以二叉树结构表征函数项,运用正交最小二乘算法(OLS)估计二叉树分支对于模型精度的贡献并据此去除复杂、冗余的函数项,从而加快遗传规划的收敛速度,最后通过GP进化可获得简单、可靠、准确的线参数非线性线参数动态模型.实际发动机起动过程建模应用案例证明,这种思路对于具有非线性输入输出关系特征的数据可以产生鲁棒性好、解析性强的线参数非线性模型.

Aero-engine nonlinear dynamic starting model based on parsimonious genetic programming

A novel Parsimonious Genetic Programming(PGP) algorithm for aero-engine starting process optimum data-driven nonlinear dynamic modeling was proposed.First, the method used the traditional GP to generate nonlinear input-output models that were represented in a binary tree structure.Then,Orthogonal Least Squares algorithm(OLS) was applied to estimate the contribution of the tree branches to the accuracy of the model.On this basis,the complex redundant subtrees can be eliminated and the GP's convergence speed is enhanced.Finally,the simple,reliable and accurate linear-in-parameters nonlinear dynamic model can be obtained via GP evolution.Applications to real aeroengine starting process modeling validate that the proposed method can generate more robust and interpretable models.