Determining Topology Architecture for Chaotic Time Series Neural Network

Li Qing; Zheng Deling

doi:10.13374/j.issn1001-053x.1999.01.026

Volume 21 Issue 1

Aug. 2021

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Article Navigation > Chinese Journal of Engineering > 1999 > 21(1): 90-93

Li Qing, Zheng Deling. Determining Topology Architecture for Chaotic Time Series Neural Network[J]. Chinese Journal of Engineering, 1999, 21(1): 90-93. doi: 10.13374/j.issn1001-053x.1999.01.026

Citation:

Li Qing, Zheng Deling. Determining Topology Architecture for Chaotic Time Series Neural Network[J]. Chinese Journal of Engineering, 1999, 21(1): 90-93. doi: 10.13374/j.issn1001-053x.1999.01.026

Li Qing, Zheng Deling. Determining Topology Architecture for Chaotic Time Series Neural Network[J]. Chinese Journal of Engineering, 1999, 21(1): 90-93. doi: 10.13374/j.issn1001-053x.1999.01.026

Citation:

Li Qing, Zheng Deling. Determining Topology Architecture for Chaotic Time Series Neural Network[J]. Chinese Journal of Engineering, 1999, 21(1): 90-93. doi: 10.13374/j.issn1001-053x.1999.01.026

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Determining Topology Architecture for Chaotic Time Series Neural Network

doi: 10.13374/j.issn1001-053x.1999.01.026

Information Engineering School, UST Beijing, Beijing 100083, China

Received Date: 1998-05-05
Available Online: 2021-08-27

Abstract

Abstract

Three-layered feed-forward neural network is used to establish the model for chaotic time series dynamic systems. Determination method of topology architecture for network is given.The size of the input node and hidden node is determined by the minimization the generalization error of the network. The simulation results show that the network architecture is optimized and the generalization error is dramatically decreased when this method is used.
- chaotic time series neural network,
- topology architecture,
- generalization error,