两种特征扩展过程神经元网络应用比较研究

Comparison of Two Charateristics Extended Process Neural Networks

过程神经元网络(Process Neural Networks)的输入由几何点式数据变为时间信号,是瞬时输入到过程输入的推广,可看作是学习样本特征的扩展。采用基于时域特征扩展和基于正交分解特征扩展两种方式建立过程神经元网络模型,利用电力负荷数据进行网络训练和负荷预测。结果表明,基于正交分解特征扩展的过程神经元网络在训练速度、预测准确度等方面均优于基于时域特征扩展的过程神经元网络,更适于电力负荷预测应用。

The input of process neural networks changed from discrete data to veritable data.As an extension to process input from instantaneous input on essence,the input can be considered as generalization of learn sample characters,and aggrandizing the sample information.The two different ways based on the time-domain feature expansion and the orthogonal decomposition feature expansion are used to establish process neural networks model.According to the data of student electric consumption in Jiangnan University,the model training and the accuracy of load forcasting are investigated.The simulation results show that the orthogonal decomposition feature expansion based process neural networks is supprior to time-domain feture expansion based proscess neural networks in training speed,veracity in prediction,and more suitable for the application of electric load forecasting.