一种用于非线性复杂系统辨识的自适应模糊神经网络

AN ADAPTIVE FUZZY NEURAL NETWORK FOR IDENTIFICATION OF THE COMPLICATED NONLINEAR SYSTEM

该文提出一种用于复杂的非线性未知系统辨识的混合神经网络模型—自适应模糊神经网络(AFNN)。AFNN网络结构简洁,具有通用逼近的特性,能够克服由于突变点的存在而对系统辨识所带来的误差,提高整个系统的辨识精度。对空空导弹攻击区辨识的仿真结果验证了AFNN网络的有效性。

This paper presents a compound neural network model, i.e., adaptive fuzzy neural network (AFNN), which can be used for identifying the complicated nonlinear system. AFNN has a simple structure and possesses the ability of universal approximation. It is capable of overcoming the error of system identification due to the existence of some changing points and improving the accuracy of identification of the whole system. The effectiveness of the model is tested on the identification result of missile attacking area.