Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/1626
Title: | Echo state Gaussian process |
Authors: | Demiris, Yiannis Chatzis, Sotirios P. |
Major Field of Science: | Engineering and Technology |
Field Category: | Electrical Engineering - Electronic Engineering - Information Engineering |
Keywords: | Bayesian inference;Reservoir computing;Gaussian processes;Sequential data modeling |
Issue Date: | Sep-2011 |
Source: | IEEE transactions on neural networks, 2011, vol. 22, no. 9, pp. 1435-1445 |
Volume: | 22 |
Issue: | 9 |
Start page: | 1435 |
End page: | 1445 |
Journal: | IEEE transactions on neural networks and learning systems |
Abstract: | Echo state networks (ESNs) constitute a novel approach to recurrent neural network (RNN) training, with an RNN (the reservoir) being generated randomly, and only a readout being trained using a simple computationally efficient algorithm. ESNs have greatly facilitated the practical application of RNNs, outperforming classical approaches on a number of benchmark tasks. In this paper, we introduce a novel Bayesian approach toward ESNs, the echo state Gaussian process (ESGP). The ESGP combines the merits of ESNs and Gaussian processes to provide a more robust alternative to conventional reservoir computing networks while also offering a measure of confidence on the generated predictions (in the form of a predictive distribution). We exhibit the merits of our approach in a number of applications, considering both benchmark datasets and real-world applications, where we show that our method offers a significant enhancement in the dynamical data modeling capabilities of ESNs. Additionally, we also show that our method is orders of magnitude more computationally efficient compared to existing Gaussian process-based methods for dynamical data modeling, without compromises in the obtained predictive performance |
URI: | https://hdl.handle.net/20.500.14279/1626 |
ISSN: | 10459227 |
DOI: | 10.1109/TNN.2011.2162109 |
Rights: | © IEEE |
Type: | Article |
Affiliation : | Imperial College London |
Publication Type: | Peer Reviewed |
Appears in Collections: | Άρθρα/Articles |
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