线性网络部分的参数采用递推最小二乘法辨识，多层前向网络的权值和阈值采用BP算法学习。
The parameters of linear network are identified by recursive least square and weights and thresholds of MFNN are learned by BP algorithm.
在此基础上利用递推最小二乘算法辨识出模型的结论参数。
The conclusion parameters are identified by the recursive least-square identification algorithm.
许多用于动态系统在线参数估计的递推辨识算法，同样可以用于离线数据拟合问题。
Many recursive identification algorithms used for on-line estimation of parametersin dynamic systems can also be applied to data fitting for off-line problems.
本文给出这类系统的参数递推辨识算法，克服了迭代算法不能在线运行、需反复矩阵求逆的不足。
This paper presents a recursive parameter identification algorithm for the system. Compared with the iterative algorithm, it can avoid the matrix inversion and can be operated on-line.
本文给出这类系统的参数递推辨识算法，克服了迭代算法不能在线运行、需反复矩阵求逆的不足。
This paper presents a recursive parameter identification algorithm for the system. Compared with the iterative algorithm, it can avoid the matrix inversion and can be operated on-line.
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