Name
Title | ||
|---|---|---|
Short-term data-based spatial parallel autoreservoir computing on spatiotemporally chaotic system prediction |
Abstract | ||
|---|---|---|
This paper presents a novel machine learning method for predicting chaotic spatiotemporal systems by proposing a parallel reservoir-like neural network. In contrast to previous machine learning methods, which require big data and suffer from high computing costs, the main advantage of the method is that it is based on short-term data and only needs to train the weight of the output layer. Theoretically, the ratio of training data length and the prediction data length can reach 2:1. First, the method for transforming an infinite-dimensional system into a finite-dimensional system is introduced. Then, based on this concept and the spatiotemporal information transformation, a network training algorithm of spatial parallel autoreservoir computing structure is proposed. Numerical experiments verified that with short-term data, the proposed method performs better than some widely used data-driven methods. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s00521-021-06854-2 | Neural Computing and Applications |
Keywords | DocType | Volume |
Reservoir computing, Spatiotemporally chaotic system, Data-driven method | Journal | 34 |
Issue | ISSN | Citations |
11 | 0941-0643 | 0 |
PageRank | References | Authors |
0.34 | 5 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yin Wang | 1 | 0 | 0.34 |
| Shutang Liu | 2 | 51 | 11.49 |