To solve the problem of the robust prediction of neural networks, the paper proposed a universal method of nonlinear model identification.
为了实现神经网络预测模型的鲁棒预测，提出一种基于非线性偏自相关的一般化预测模型辨识方法。
The parameter estimation and system identification of nonlinear structures is a subject which serves to obtain and verify mathematical model of nonlinear structures.
非线性结构的参数估计和系统识别是取得和验证非线性结构数学模型的一门学科。
Simulation results based on ideal mathematical model and industrial model show that the PID Elman network is prior to the modified Elman network in identification of nonlinear dynamic systems.
无论是理想的数学模型还是实际工业模型，计算机仿真结果均证明，将P ID型网络用于动态系统辨识具有更好的逼近效果。
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