基于遗传规划的哈默斯坦模型的辨识

该文基于遗传规划提出了一种辨识哈默斯坦模型的新方法。哈默斯坦模型由静态非线性模块和动态线性模块串联而成,因此系统辨识的目标是要找到非线性和线性模块的最优数学模型。该文通过遗传规划确定非线性模块的函数结构,并结合遗传算法确定模型的未知参数,适应度值的计算采用了最小信息量准则(A IC),以平衡模型的复杂度和精确度。该方法不需要对模型的先验知识有详细了解,就能达到较好的辨识效果,并且能够克服观测噪声的污染,获得参数的无偏估计。仿真结果说明了该方法的有效性。

Hammerstein Model Identification Using Genetic Programming

The paper proposes a novel method for the identification of Hammerstein model based on genetic programming.The Hammerstein model is composed of a nonlinear static block in series with a linear dynamic block,so the aim of system identification is to obtain the optimal mathematical model of these two blocks.In this paper,genetic programming is used to estimate the functional structure for nonlinear static block,and the unknown parameters of the model are estimated with genetic algorithm.The fitness is evaluated by AIC(Akaike information criterion) to balance the complexity and accuracy of the model.Using the method,good identification results can be got without enough prior knowledge of the model,and unbiased parameter estimates can be obtained when the output is corrupted by random noise uncorrelated with the input signal.Simulation results show that the method is very efficient.