Name
Abstract | ||
|---|---|---|
Reservoir Computing (RC) is a high-speed machine learning framework for temporal data processing. Especially, the Echo State Network (ESN), which is one of the RC models, has been successfully applied to many temporal tasks. However, its prediction ability depends heavily on hyperparameter values. In this work, we propose a new ESN training method inspired by Generative Adversarial Networks (GANs). Our method intends to minimize the difference between the distribution of teacher data and that of generated samples, and therefore we can generate samples that reflect the dynamics in the teacher data. We apply a feedforward neural network as a discriminator so that we don't need to use backpropagation through time in training. We justify the effectiveness of the proposed method in time series prediction tasks. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-30493-5_8 | ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2019: WORKSHOP AND SPECIAL SESSIONS |
Keywords | Field | DocType |
Echo State Network, Recurrent Neural Network, Generative Adversarial Network, Nonlinear time series prediction | Backpropagation through time,Feedforward neural network,Discriminator,Hyperparameter,Computer science,Recurrent neural network,Temporal database,Artificial intelligence,Reservoir computing,Echo state network,Machine learning | Conference |
Volume | ISSN | Citations |
11731 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takanori Akiyama | 1 | 0 | 0.34 |
| Gouhei Tanaka | 2 | 51 | 11.80 |