摘要
为了提高非线性Hammerstein模型的辨识精度，提出一种利用混合优化算法对非线性模型进行辨识的新方法。该算法的基本思想是把非线性系统的参数辨识问题转化为参数空间上的函数优化问题，然后利用遗传算法和改进的粒子群优化算法相结合寻求并获得参数问题的最优解。最后通过仿真研究表明，该方法对于非线性辨识具有较好的有效性和鲁棒性，获得了良好的辨识效果，是一种可行的解决非线性辨识问题的方法。
In order to improve the identification accuracy of nonlinear Hammerstein model, a new method of the hybrid optimization algorithm identifying the nonlinear model is put forward. The basic idea of the algorithm is to put the identification problem of parameters in the nonlinear system into a parameter space function optimization problem, and then using hybrid algorithm of the genetic algorithm and the improved particle swarm optimization algorithm to obtain the optimal solution of parameters problem. Finally, the simulation results show that the method for nonlinear identification has good effectiveness and robustness, get a good recognition effect, and is a feasible method to solve the problem of nonlinear recognition.
出处
《计量学报》
CSCD
北大核心
2015年第4期418-422,共5页
Acta Metrologica Sinica
