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提出一种将前馈神经网络用于非线性时变系统辩识的学习算法,其要点是把网络权值看作时变系统的一个状态,用卡尔曼滤波估计此状态,于是网络实现了非线性和时变的映射.文中推导了该算法,仿真结果证实了它的有效性.“,”As it is wel l known,it is difficult to identify a nonlinear time varying system using tradit ional identification approaches,especially under unknown nonlinear function.Neur a l networks have recently emerged as a successful tool in the area of identificat ion and control of time invariant nonlinear systems.However,it is still difficul t to apply them to complicated time varying system identification.In this paper we present a learning algorithm for identification of the nonlinear time varying system using feedforward neural networks.The main idea of this approach is that we regard the weights of the network as a state of a time varying system,then u se a Kalman filter to estimate the state.Thus the network implements nonlinear a nd time varying mapping.We derived both the global and local learning algo rithms.Simulation results demonstrate the effectiveness of this approach.