Abstract
During the last years, there has been a growing interest in the Reservoir Computing (RC) paradigm. Recently, a new RC model was presented under the name of Echo State Queueing Networks (ESQN). This model merges ideas from Queueing Theory and one of the two pioneering RC techniques, Echo State Networks. In a RC model there is a dynamical system called reservoir which serves to expand the input data into a larger space. This expansion can enhance the linear separability of the data. In the case of ESQN, the reservoir is a Recurrent Neural Network composed of spiking neurons which fire positive and negative signals. Unlike other RC models, an analysis of the dynamics behavior of the ESQN system is still to be done. In this work, we present an experimental analysis of these dynamics. In particular, we study the impact of the spectral radius of the reservoir in system stability. In our experiments, we use a range of benchmark time series data.
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lukoševičius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Computer Science Review, 127–149 (2009)
Basterrech, S., Rubino, G.: Echo State Queueing Network: a new Reservoir Computing learning tool. In: IEEE Consumer Comunications & Networking Conference, CCNC 2013 (January 2013), doi:10.1109/CCNC.2013.6488435
Jaeger, H.: The “echo state” approach to analysing and training recurrent neural networks. Technical Report 148, German National Research Center for Information Technology (2001)
Jaeger, H., Maass, W., Príncipe, J.C.: Special issue on echo state networks and liquid state machines - editorial. Neural Networks (3), 287–289 (2007)
Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: a new framework for a neural computation based on perturbations. Neural Computation, 2531–2560 (November 2002)
Verstraeten, D., Schrauwen, B., D’Haene, M., Stroobandt, D.: An experimental unification of reservoir computing methods. Neural Networks (3), 287–289 (2007)
Cortes, C., Vapnik, V.: Support-Vector Networks. Mach. Learn. 20(3), 273–297 (1995)
Maass, W.: Noisy spiking neurons with temporal coding have more computational power than sigmoidal neurons. Technical Report TR–1999–037, Institute for Theorical Computer Science. Technische Universitaet Graz, Graz, Austria (1999)
Gelenbe, E.: Random Neural Networks with Negative and Positive Signals and Product Form Solution. Neural Computation 1(4), 502–510 (1989)
Timotheou, S.: The random neural network: A survey. The Computer Journal 53(3), 251–267 (2010)
Gelenbe, E.: Learning in the Recurrent Random Neural Network. Neural Computation 5(1), 154–511 (1993)
Basterrech, S., Mohamed, S., Rubino, G., Soliman, M.: Levenberg-Marquardt Training Algorithms for Random Neural Networks. Computer Journal 54(1), 125–135 (2011)
Rodan, A., Tiňo, P.: Minimum Complexity Echo State Network. IEEE Transactions on Neural Networks, 131–144 (2011)
Cortez, P., Rio, M., Rocha, M., Sousa, P.: Multiscale Internet traffic forecasting using Neural Networks and time series methods. Expert Systems (2012)
Basterrech, S., Fyfe, C., Rubino, G.: Self-Organizing Maps and Scale-Invariant Maps in Echo State Networks. In: 2011 11th International Conference on Intelligent Systems Design and Applications (ISDA), pp. 94–99 (November 2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Basterrech, S., Snášel, V., Rubino, G. (2014). An Experimental Analysis of Reservoir Parameters of the Echo State Queueing Network Model. In: Abraham, A., Krömer, P., Snášel, V. (eds) Innovations in Bio-inspired Computing and Applications. Advances in Intelligent Systems and Computing, vol 237. Springer, Cham. https://doi.org/10.1007/978-3-319-01781-5_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-01781-5_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01780-8
Online ISBN: 978-3-319-01781-5
eBook Packages: EngineeringEngineering (R0)