动态神经网络在变形预报中的应用

Application of Dynamic Neural Network in Prediction Model

静态神经网络模型用于在线时间序列的预报时具有局限性,即网络的泛化能力有限,且模型不能不断地适应新增样本的变化。如果每增加一个样本对神经网络重新训练,需要大量的计算时间。针对该问题,提出了动态神经网络预报模型。在获得新增样本数据之后,通过比较预报值与实际值之差的绝对值是否大于ε敏感因子,决定模型是否需要修正。为了降低模型修正的计算时间,提出了在线动态修正方法,实现了增加样本而矩阵阶数不增加,且避免了矩阵求逆运算,理论上可以提高计算效率。通过实例表明,该方法在计算时间和预报精度两个方面都具有一定优势,可应用于在线实时变形预报及相关领域。

Batch implementations of artificial neural network （ANN） are inefficient when used in an online setting because they must be retrained from scratch every time the training set is modified. In order to reduce the expense of ANN training, we have developed a dy- namic neural network （DNN） modeling method for online time series prediction. Dynamic neural network modeling that efficiently updates a trained static neural network whenever a sample is added to the training set. The updated ANN function is identical to that produced by a batch algorithm. Application of DNN in an online scenario is presented. In the scenario, numerical experiments indicate that DNN is faster than batch ANN algorithms obviously.