联想神经网络的风速序列预测分析

为了提高风速序列预测的可靠性,针对具有混沌特性的风速序列,构造了一种用于风速序列预测的联想网络。以风速序列的波动性作为相似性测度准则,构造联想网络的存储样本模式,根据存储模式中蕴含的关联信息完成网络的无监督学习,从而完成具有自相似性的风速序列的一步或多步预测分析。与传统前向型神经网络相比,该网络预测机理明确,预测结果唯一,且可一次给出多步预测结果。仿真实验结果表明,该网络的具有良好预测性能,适用于风速序列的动态预测。

Wind speed time series prediction based on associative network

In order to improve the reliability of wind speed series prediction, a new associative network was constructed to predict the wind speed series with chaotic characteristics. Stored sample patterns were constructed according to the similarity measure of the volatile of the wind speed series. Utilizing the correlation information contained in the stored sample patterns, the network adopts an unsupervised learning algorithm to complete the weight training. One step or multi-step prediction of the wind speed series which have self-similarity can be completed by the associative network. Compared with the conventional forward neural network, the prediction mechanism of the associative prediction network is explicit, and the prediction result is uniqueness. The network can also give one step or multi-step prediction results simultaneously in once calculation. Simulation results show that the associative network has good prediction performance, and can be applied to predict dynamically the wind speed series.