Determining the input dimension of a neural network for nonlinear time series prediction |
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Authors: | Zhang Sheng Liu Hong-Xing Gao Dun-Tang and Du Si-Dan |
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Institution: | Department of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China; Department of Physics, Nanjing Normal University, Nanjing 210097, China |
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Abstract: | Determining the input dimension of a feed-forward neural network for nonlinear time series prediction plays an important role in the modelling. The paper first summarizes the current methods for determining the input dimension of the neural network. Then inspired by the fact that the correlation dimension of a nonlinear dynamic system is the most important feature of it, the paper presents a new idea that the input dimension of the neural network for nonlinear time series prediction can be taken as an integer just greater than or equal to the correlation dimension. Finally, some validation examples and results are given. |
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Keywords: | nonlinear time series prediction phase space reconstruction neural network input dimension |
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