Abstract
The introduction of the full paper points out that the prediction model based on ESN (echo state network) proposed by H. Jaeger in Ref. 1 is, in our opinion, not good enough. So we propose what we believe to be a new and better prediction model based on ESN and PCA (principal component analysis). Section 1 briefs the relevant information in Refs. 2 through 6. Section 2 explains our prediction model; its block diagram is given in Fig. 2. Section 3 gives eq. (6) for measuring the prediction precision of our prediction model. Section 4 compares the simulation results of our prediction model with those of the prediction model based on ESN. The simulation results, given in Figs. 4 through 7 and in Tables 1, 2 and 3, and their analysis show preliminarily that: (1) the computing complexity of our prediction model is lower; (2) its training time is shorter; (3)the prediction rate is higher than that of the prediction model based on ESN.
Original language | English |
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Pages (from-to) | 946-951 |
Number of pages | 6 |
Journal | Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University |
Volume | 28 |
Issue number | 6 |
State | Published - Dec 2010 |
Keywords
- Chaotic time series
- Echo state network(ESN)
- Prediction model
- Principal component analysis