基于自校正回归神经元网络的预报建模
PREDICTION MODELING BASED ON RECURREN TNEURAL NETWORKS WITH SELF-TUNING FUNCTION
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摘要: 讨论了回归神经元网络（RNN）的网络结构和基本实现方法，提出了主元分析（PCA）和具有自校正功能的回归神经元网络相结合的非线性时变系统预报建模方法，并用于减压塔塔顶温度的预报．结果表明，该方法具有良好的预报性能．Abstract: This paper discusses the architecture and algorithm of recurrent neural networks(RNN) and proposes an approach of prediction modeling for non-linear time-varying system based on the recurrent neural networks with self-tuning function in combination with principal component analysis. The method is applied to predict the top temperature of vacuum distillation column and has been proven to have better performance than other methods.
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