Two prototype algorithms of iterative learningidentification, iterative learning Bayes and stochastic Newton algorithms, are proposed with detail.
推导得到两种迭代学习辨识算法：迭代学习贝叶斯法和迭代学习随机牛顿法。
2
Simulation results illustrate that LM algorithm speed up learning process and reduce training time greatly. The identification effect is very good.
仿真结果表明LM算法可大大地提高学习速度，缩短训练时间，且辨识效果很好。
3
RBF neural network provides an effective means for system identification and modeling with its advantages of smaller calculation quantity and high learning speed.
R BF神经网络以其计算量小，学习速度快，不易陷入局部极小等诸多优点为系统辨识与建模提供了一种有效的手段。