Abstract
Recently a new Neural Network model named Reservoir with Random Static Projections (R2SP) was introduced in the literature. The method belongs to the popular family of Reservoir Computing (RC) models. The R2SP method is a combination of the RC models and Extreme Learning Machines (ELMs). In this article, we analyse the accuracy of a variation of the R2SP that consists of using Radial Basis Functions (RBF) projections instead of ELMs. We evaluate the proposed variation on two simulated benchmark problems obtaining promising results with respect to other RC models.
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Acknowledgment
This article has been elaborated in the framework of the project New creative teams in priorities of scientific research, reg. no. CZ.1.07/2.3.00/30.0055, supported by Operational Programme Education for Competitiveness and co-financed by the European Social Fund and the state budget of the Czech Republic and supported by the IT4Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070), funded by the European Regional Development Fund and the national budget of the Czech Republic via the Research and Development for Innovations Operational Programme.
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Basterrech, S., Rubino, G., Snášel, V. (2016). Experimental Analysis of a Hybrid Reservoir Computing Technique. In: Abraham, A., Han, S., Al-Sharhan, S., Liu, H. (eds) Hybrid Intelligent Systems. HIS 2016. Advances in Intelligent Systems and Computing, vol 420. Springer, Cham. https://doi.org/10.1007/978-3-319-27221-4_20
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DOI: https://doi.org/10.1007/978-3-319-27221-4_20
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