Time series prediction with a neural network model based on bidirectional computation style: An analytical study and its estimation on acquired signal transformation
Time series prediction with a neural network model based on bidirectional computation style: An analytical study and its estimation on acquired signal transformation
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基于双向计算风格的神经网络模型的时间序列预测：对采集信号变换的分析研究及其估计
DOI：
10.1002/eej.10232
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发表时间：
2003
影响因子：
0.4
通讯作者：
K. Shida
中科院分区：
文献类型：
--
作者：
H. Wakuya;K. Shida
许多研究人员对时间序列预测进行了大量的研究。它们中的大多数通常使用单向计算流，即，当前信号作为输入应用于模型，并且预测的未来信号作为输出从模型导出。相反，最近提出了双向计算风格并应用于预测任务。双向神经网络模型由两个相互连接的子网络组成，并双向执行正逆变换。为了将该模型应用于时间序列预测任务，一个子网络被训练为传统的未来预测任务，另一个子网络被训练为用于过去预测的附加任务。由于未来和过去的预测子系统之间的耦合作用，提高了模型的信号处理能力，双向的计算架构，使它有可能提高其性能。此外，为了研究所获得的信号的转换，本文采用了两种混沌时间序列-麦基-格拉斯时间序列和“数据集A”。作为计算机模拟的结果，它已被实验发现，独立开发的直接和逆变换和它们的信息集成使双向模型优于单向模型。© 2003 Wiley Periodicals，Inc. Electr Eng Jpn，145（3）：50-60，2003;在线发表于Wiley InterScience（www.interscience.wiley.com）。DOI 10.1002/eej.10232
Numerous studies on time series prediction have been undertaken by a variety of researchers. Most of them typically used unidirectional computation flow, that is, present signals are applied to the model as an input and predicted future signals are derived from the model as an output. On the contrary, bidirectional computation style has been proposed recently and applied to prediction tasks. A bidirectional neural network model consists of two mutually connected subnetworks and performs direct and inverse transformations bidirectionally. To apply this model to time series prediction tasks, one subnetwork is trained a conventional future prediction task and the other is trained an additional task for past prediction. Since the coupling effects between the future and past prediction subsystems promote the model's signal processing ability, bidirectionalization of the computing architecture makes it possible to improve its performance. Furthermore, in order to investigate the acquired signal transformation, two kinds of chaotic time series—the Mackey–Glass time series and “Data Set A”—are adopted in this paper. As a result of computer simulations, it has been found experimentally that the direct and inverse transformations developed independently and their information integration give the bidirectional model an advantage over the unidirectional one. © 2003 Wiley Periodicals, Inc. Electr Eng Jpn, 145(3): 50–60, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10232