模糊神经网络用于非线性系统模型辨识

【摘要】提出了一种非线性系统的模型辨识方法。在只有被辨识系统的输入输出数据的情况下，利用一种无监督的聚类算法来进行结构辨识，从而自动获得模糊规则库，并可以得到模糊系统的初始参数。在聚类的基础上，构造一个与之相匹配的模糊神经网络，用它的学习算法来训练网络得到一个精确的模糊模型，从而实现参数辨识。同时，证明了所构造的模糊神经网络具有通用逼近能力，这个能力在模糊建模和模糊控制方面非常有用。通过对两个非线性系统辨识的仿真结果验证了该方法的有效性。
关 键 词 模糊神经网络; 结构辨识; 参数辨识; 系统辨识

In the last few years, based on neural network and fuzzy system, a lot of new system identification and control methods of nonlinear system have been proposed [1～5]. Despite the fact that these methods are effective in some application area, most of them are only used in parameter identification not in structure identification. The proposed model identification of nonlinear system is composed of two parts: structure identification and parameter identification. At the same time, this FNN has universal approximation capability, a property very useful in, e.g., modeling and control applications.