研究用微分方程数值解法——线性多步法替代神经网络的学习算法。
Training neural networks by using the linear multi-step method is studied, which is a classical numerical method for differential dynamics.
对于整数阶常微分方程的数值解法，如欧拉法、线性多步法等都已有较完善的理论。
Numerical method of integral order ordinary differential equation, for example, Euler method, linear multiple step method, and so on, has had quite perfect theories.
最后给出了一些数值例子，证实了这个分数阶线性多步法是解分数阶常微分方程的一个有效方法。
Finally, some numerical examples are provided to show that the fractional order linear multiple step method for solving the fractional order ordinary differential equation is an effective method.
最后给出了一些数值例子，证实了这个分数阶线性多步法是解分数阶常微分方程的一个有效方法。
Finally, some numerical examples are provided to show that the fractional order linear multiple step method for solving the fractional order ordinary differential equation is an effective method.
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