模糊神经网络用于非线性系统模型辨识(英文)

Nonlinear-Systems Model Identification with Additive- Multiplicative Fuzzy Neural Network

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。

A model identification approach of nonlinear systems where only the input-output data of the identified system are available is presented. To automatically acquire the fuzzy rule-base and the initial parameters of the fuzzy model, an unsupervised clustering method is used in structure identification. Based on the cluster result, a Fuzzy Neural Network (FNN) is constructed to match with it. The FNN is trained by its learning algorithm to obtain a precise fuzzy model and realize parameter identification. Finally, the effectiveness of the proposed technique is confirmed by the simulation results of two nonlinear systems.