基于支持向量机和粒子群算法的压气机特性计算

改进和实现了支持向量机用于函数回归估计的算法，并将支持向量机和粒子群优化算法运用于压气机特性计算，以寻找压气机进口换算空气流量和效率随增压比、转子转速、压气机进气角度变化的非线性函数关系，将进气角度作为一个影响因子列入考虑范围，改进了以往只考虑压气机特性随转子转速和增压比变化的计算方法。用支持向量机回归估计压气机特性随各因子变化的非线性函数，用粒子群优化算法对非线性函数逼近度进行全局优化，计算结果表明，设计的算法能准确地回归估计出描述压气机特性的非线性函数，算法是有效的。 

Method to achieve compressor characteristics maps based on SVM and PSO

An algorithm for support vector regression is improved and implemented as in computer programming. And this new algorithm, together with the particle swarm optimization, is used to regress the nonlinear function about how would compressor efficiency or corrected inlet air flow vary when the rotor speed, pressure ratio and compressor inlet flow angle changed. Here the inlet flow angle is considered as one of the independent variables, so this is the improvement for the method to achieve the compressor characteristics maps only depending on rotor speed and pressure ratio. The nonlinear relationship between the dependent variables and independent ones described above was regressed by support vector machine, and its degree of approximation was optimized by particle swarm optimization. Finally, the method was tested to be effective to achieve the nonlinear function describing the compressor characteristics maps.